Dynamic Load Profiling for Plug-in Electric Vehicles
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Solution Overview
Problem
Traditional power distribution systems struggle to accurately predict and manage dynamic loads from plug-in electric vehicles (PEVs) due to their mobile nature, leading to instability in power grids, especially during peak usage times.
Innovation Solution
A system and method for dynamic load profiling that utilizes GPS data from PEVs to predict their locations and charging needs, integrating this information with static load data to generate accurate forecasts and adjust power distribution accordingly, involving a processor-based system that communicates with a distribution control center and geographic information system to optimize power allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring technology is used to forecast power distribution, then static load prediction is simple and cost-effective, but dynamic load from mobile PEVs cannot be accurately forecasted
Solution Approach 1:
The system segments the load forecasting problem into static load components (forecasted using conventional methods) and dynamic load components (forecasted using mobile device location data). This segmentation allows each component to be handled with appropriate methods, improving overall accuracy without requiring complete system redesign.
Solution Approach 2:
The system introduces mobile device location data as an intermediary element that bridges the gap between static forecasting methods and dynamic load requirements. By using GPS and location information from mobile devices, the system can predict PEV locations and charging demands without directly monitoring each vehicle.
2Reliability
If PEV charging demands are not accurately predicted, then system operation is simpler, but grid stability deteriorates during peak usage times
Solution Approach 1:
The system performs preliminary forecasting of PEV charging demands by analyzing mobile device location patterns and predicting future PEV locations. This advance prediction allows the utility company to prepare for peak loads and maintain grid stability before the actual charging demands occur.
Solution Approach 2:
The system continuously monitors mobile device locations and compares predicted PEV locations with actual locations, using this feedback to refine forecasting accuracy. This feedback mechanism improves grid stability by enabling real-time adjustments to power distribution based on actual versus predicted demands.
3Measurement precision
If detailed tracking of individual PEV locations is implemented, then load profiling accuracy is improved, but information processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the essential location information from mobile devices that is needed for PEV identification and tracking. By selectively extracting GPS coordinates and location patterns rather than processing all available device data, the system achieves accurate load profiling while minimizing data processing burden.
Data Source
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AI summary
A method for dynamic load profiling of plug-in electric vehicles (PEVs) (105) in a power network can include receiving static load data in the power network, generating a load forecast from the static load data, generating dynamic load data from data related to PEVs (105) in the power network and modifying the load forecast based on the dynamic load data for profiling the dynamic load data of the PEVs (105).