EV Charge Load Estimation for Grid Stability
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Solution Overview
Problem
Utilities face challenges in accurately managing the increasing load associated with electric vehicles on the electric grid due to their mobile nature and varying charging characteristics, making it difficult to balance electrical demand effectively.
Innovation Solution
A computing device and method that estimate the charge load and time for electric vehicles by storing their charging characteristics, determining the state of charge, and facilitating authorization to charge based on peak charge times and loads, thereby providing utilities with accurate data to manage grid demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If electric vehicles are allowed to charge freely without estimation, then charging accessibility is improved, but grid load balance deteriorates
Solution Approach 1:
The system performs preliminary estimation of charge load and charge time before authorization is granted. By calculating these parameters in advance based on vehicle state and charging characteristics, the system can predict grid impact and make informed authorization decisions, preventing overload while maintaining accessibility.
Solution Approach 2:
The system establishes a feedback loop where charge load estimations are continuously calculated and used to inform authorization decisions. The utility receives estimation data and can adjust authorization policies based on real-time and historical load patterns, creating a dynamic balance between accessibility and grid stability.
2Stability of the object's composition
If charge load estimation is implemented, then grid load balance is improved, but system complexity increases
Solution Approach 1:
The system divides the charge load estimation process into distinct functional modules: data collection from vehicles, calculation of charge load based on vehicle state and charging characteristics, determination of charge time, and authorization decision-making. This segmentation allows each component to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The system introduces an intermediary estimation layer between the vehicle charging request and the grid. This intermediary calculates charge load and time parameters, translating vehicle charging needs into grid-compliant authorization decisions, thereby simplifying the interaction between vehicles and utility infrastructure.
3Measurement precision
If accurate charging characteristics data is collected, then estimation precision is improved, but data management complexity increases
Solution Approach 1:
The system creates a universal data collection framework that gathers charging characteristics from diverse vehicle types and sources through standardized interfaces. This multi-functional approach allows the same data management infrastructure to handle various vehicle models, charging standards, and data formats, reducing overall complexity while maintaining precision.
Data Source
AI summary
A computing device for estimating a charge load and a charge time for at least one electric vehicle is described. The computing device is configured to store, in a database coupled to the computing device, charging characteristics associated with a first vehicle of the at least one electric vehicle. The computing device is also configured to receive, from the first electric vehicle, a state of charge of the first electric vehicle, determine an estimated peak charge time and an estimated peak charge load based on the charging characteristics associated with the first electric vehicle and the state of charge of the first electric vehicle, and facilitate determining whether the first electric vehicle is authorized to charge based at least on the estimated peak charge time and the estimated peak charge load.