EV Charging Control via Smart Meter Clusters and Policy Enforcement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing number of electric vehicles poses challenges to power grids due to high concentration of charging events, leading to power shortages, grid instability, and unequal electricity distribution, necessitating a method to efficiently manage and control electric vehicle charging to optimize power utilization.
Innovation Solution
A system utilizing smart meters and an advanced metering infrastructure to create electric vehicle clusters, generate policies for controlled charging, monitor for policy violations, and enforce actions to prevent uncontrolled charging, thereby managing and controlling electric vehicle charging events across distributed stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If electric vehicle charging events are concentrated in a given geographic area, then charging accessibility is improved, but power grid stability deteriorates due to uncontrolled charging events causing power shortage and overloading
Solution Approach 1:
The system implements a feedback mechanism where smart meters continuously monitor charging events and communicate with the centralized server. The server analyzes aggregated meter data to detect policy violations and sends control signals back to smart meters to regulate charging, creating a closed-loop feedback system that maintains grid stability while enabling concentrated charging infrastructure
Solution Approach 2:
A centralized server acts as an intermediary between distributed smart meters and the power grid. The server aggregates data from multiple smart meters, processes it to identify policy violations, and coordinates control actions, thereby mediating between local charging demands and overall grid stability without requiring direct complex interactions between individual charging stations
2Adaptability or versatility
If the number of electric vehicle charging stations is increased, then charging availability is improved, but difficulty in assessing power consumption requirements increases due to scattered distribution
Solution Approach 1:
The system merges data from numerous scattered smart meters into a centralized database on the server. By aggregating meter data from distributed charging stations, the system enables comprehensive assessment of power consumption requirements across the entire network, transforming individual isolated measurements into collective actionable insights
3Speed
If electric vehicle charging is performed in an uncontrolled fashion, then charging speed is improved, but power grid overloading occurs leading to power failure and unequal distribution
Solution Approach 1:
The system performs preliminary actions by pre-defining charging policies that specify maximum power limits, allowed charging hours, and geographic restrictions. These policies are established before charging events occur, enabling the system to prevent overloading proactively rather than reacting after problems arise, thus maintaining both charging speed and grid safety
Data Source
AI summary
A system and method for managing and controlling charging of electric vehicles via charging stations over an advanced metering infrastructure is provided. Smart meters are deployed in the charging stations. Electric vehicle clusters which are logical representations of at least the charging stations are created. Policies for controlling electric vehicle charging based on data obtained using the electric vehicle clusters are generated. Further, it is analyzed if meter data obtained from the smart meters using the electric vehicle clusters comply with the generated policies. The meter data is obtained using the electric vehicle clusters identified with electric vehicle charging events. Policy violation action data is generated by applying predetermined rules if it is determined that the policies are violated. The policy violation action data is then sent to the identified electric vehicle clusters for controlling electric vehicle charging.