EV DER Control Using Telematics to Prevent Grid Overload
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Solution Overview
Problem
The rapid adoption of electric vehicles (EVs) poses a challenge to the power grid, as the increased demand for electricity during charging can lead to hazardous overloading of grid components and infrastructure damage.
Innovation Solution
A system and method for managing the distribution of electrical power using distributed energy resource (DER) control software, which integrates advanced metering infrastructure (AMI) data and EV telematics data to optimize EV charging, prevent overloading, and balance peak load demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If EV charging demand is increased to accelerate EV adoption, then EV penetration rate is improved, but grid component overload risk increases
Solution Approach 1:
The system dynamically adjusts EV charging rates based on real-time grid conditions, transformer capacity, and load forecasts. The DER control software continuously monitors grid state and modifies charging parameters to prevent overload while maximizing EV adoption, transforming static charging into a dynamic, adaptive process that responds to changing grid capabilities.
Solution Approach 2:
The system changes multiple parameters including charging power levels, charging timing, and load distribution ratios based on grid conditions. By adjusting these parameters in real-time, the system optimizes the balance between EV adoption goals and grid safety constraints, allowing flexible adaptation to different operational scenarios.
2Use of energy by moving object
If EV charging load is increased to meet growing demand, then energy delivery is improved, but infrastructure damage risk increases
Solution Approach 1:
The system performs preliminary actions by forecasting EV charging demands and grid conditions in advance, then pre-positioning load management strategies. The DER control software uses predictive analytics to anticipate peak loads and prepares mitigation measures beforehand, preventing infrastructure overload before it occurs rather than reacting after damage risk materializes.
Solution Approach 2:
The DER control software acts as an intermediary layer between EV charging demands and the physical grid infrastructure. It mediates the interaction by translating raw demand signals into grid-safe charging commands, buffering the direct impact of EV loads on infrastructure and preventing harmful stress concentrations.
3Ease of operation
If peak load demand is increased during high EV usage periods, then EV charging availability is improved, but grid component overload increases
Solution Approach 1:
The system implements periodic action by distributing EV charging loads across different time periods rather than concentrating them all at peak times. The DER control software schedules charging events to occur during off-peak periods when grid capacity is available, creating a periodic pattern of charge-discharge cycles that smooths demand and reduces peak stress on transformers while maintaining overall charging availability.
Solution Approach 2:
The system segments the total EV charging demand into multiple smaller load components that can be independently managed and distributed. By breaking down the aggregate charging request into individual vehicle-level or site-level segments, the DER control software can selectively activate or delay specific segments based on real-time grid capacity, preventing any single transformer from becoming overloaded while still serving the overall charging need.
Data Source
AI summary
A method and system for managing electric vehicle (EV) distributed energy resource(s) (DER) are disclosed. A DER analytics engine may receive electricity consumption data of a plurality of sites from corresponding electricity meters of the plurality of sites, detect EV charging information based at least in part on the electricity consumption data, obtain EV telematics data of EVs associated with the EV charging information, reconcile the EV charging information and the EV telematics data, and generate, based on the reconciled EV charging information and the EV telematics data, models for at least one of continuous EV load prediction, electrical vehicle supply equipment (EVSE detection), and/or optimization for at least one of aggregated load, load per feeder, or maximum revenue for time-of-use tiers.