Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Electric vehicle real-time scheduling method considering predicted load and user demand

An electric vehicle and real-time dispatching technology, which is applied in forecasting, computing, and data processing applications, etc., can solve the problems of rare dispatching, insufficient real-time dispatching effect, and little consideration of user charging needs, etc., so as to improve the security of the power grid and reduce Good peak effect and improved peak load

Pending Publication Date: 2022-05-24
SOUTH CHINA UNIV OF TECH
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The current real-time optimization research is based on the premise that electric vehicles can participate in dispatching or only takes contracted users as the demand response objects, and rarely considers the differences in charging needs of users and the fact that they are unwilling to sign long-term incentive agreements because of the high flexibility of daily travel needs. Subscribers (Chen Lvpeng, Pan Zhenning, Yu Tao, Wang Keying. Real-time Optimal Scheduling of Large-Scale Electric Vehicles Based on Dynamic Non-Cooperative Game [J]. Automation of Electric Power Systems, 2019, 43(24): 32-40+66.) , and few technologies consider dispatching forecasted loads in real-time optimization, so that the effect of real-time dispatching is not good enough, and the response potential of electric vehicles is not fully utilized (Zhang Bingxu, Xu Gang. Grid-connected rolling of electric vehicles considering demand differences Time Domain Optimization[J].Automation of Electric Power System,2020,44(13):106-114.)

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Electric vehicle real-time scheduling method considering predicted load and user demand
  • Electric vehicle real-time scheduling method considering predicted load and user demand
  • Electric vehicle real-time scheduling method considering predicted load and user demand

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0094] A real-time scheduling method for electric vehicles that takes into account the forecast load and user demand, such as figure 1 shown, including the following steps:

[0095] S1. Classify electric vehicle users according to whether they have signed a contract with an electric vehicle aggregator and the difference in charging demand, and establish charging control models for various types of electric vehicle users accordingly;

[0096] Taking the lithium battery as the object, ignoring the battery self-discharge process and approximating that the battery keeps the charging power constant in each optimization period, the charging model of the electric vehicle is established, as follows:

[0097]

[0098] Among them, S ne Demand for charging electricity for electric vehicles; S 0 is the initial state of charge (SOC) when the electric vehicle is connected; S ex is the expected state of charge when the electric vehicle leaves; C 0 is the battery capacity; S(t) is the ...

Embodiment 2

[0176] In this embodiment, when the proportion of contracted users is 30%, the total load curve of whether ordinary users participate in the response or not is as follows: Figure 4 The optimization results are shown in Table 3.

[0177] table 3

[0178]

[0179] It can be seen that compared with not participating in the response, the total load peak clipping rate increased from 2.27% to 7.68% when ordinary users participated in the response. Because when ordinary users participate in scheduling, the schedulable potential of each time period is greatly increased, so the load peak is greatly reduced. When ordinary users do not participate in the response, there are too few dispatchable EVs to meet the grid demand, and the grid will reduce subsidies to aggregators, resulting in a decrease in the aggregator’s revenue and revenue increase ratio, and the average revenue and average revenue increase ratio of EV participating in the response. It has also declined, so it is neces...

Embodiment 3

[0181] In this embodiment, when the proportion of contracted users is different, the optimization effect of real-time scheduling is also different. Figure 5 It shows the load curve of ordinary users participating in the response when the proportion of subscribers is 15%, 30% and 45% respectively. The optimization results are shown in Table 4.

[0182] Table 4

[0183]

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an electric vehicle real-time scheduling method considering predicted load and user demand. The method comprises the following steps: classifying electric vehicle users according to whether signing with an electric vehicle aggregator and charging demand differences, and establishing charging control models of various electric vehicle users according to the classification; establishing a relation model of the non-signed user response probability and the compensation electricity price; establishing subsidy mechanisms of the power grid to the aggregator and the aggregator to the electric vehicle user participation demand response; calculating response potentials of various electric vehicle users, determining power grid side scheduling demands of each time period according to the predicted load, and formulating a real-time scheduling scheme of each time period according to the power grid side scheduling demands and the electric vehicle user potentials; and establishing a real-time optimization model, and solving the charging power of the electric vehicle by adopting a particle swarm algorithm. According to the method, a good load peak clipping effect can be realized on the basis of meeting different charging requirements of electric vehicle users, and the safety of a power grid can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of electric power system demand response, and in particular relates to a real-time scheduling method for electric vehicles that takes into account predicted loads and user demands. Background technique [0002] Due to the continuous increase in the number of electric vehicles connected to the grid, the randomness and volatility of their charging loads will bring a huge operating burden to the power system. It is necessary to make electric vehicles participate in the distribution network scheduling through orderly charging to reduce load pressure and eliminate Grid overload risk. At present, the research methods of electric vehicle charging power optimization can be divided into day-ahead global optimization and real-time local optimization. Due to the nature of the moving load of electric vehicles, its arrival and departure times are random, and the state of charge of the battery has great uncertainty when ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q10/04G06Q50/06G06Q50/30
CPCG06Q10/06315G06Q10/06313G06Q10/04G06Q50/06G06Q50/40Y04S20/222
Inventor 周星月陈楚玥张勇军姚蓝霓杨景旭
Owner SOUTH CHINA UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products