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
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
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]
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com