EV Charging Detection for Transformer-Aware Load Management
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Solution Overview
Problem
The increased demand for electric vehicle charging is putting excessive strain on local low voltage distribution networks, as multiple vehicles charging simultaneously can exceed the capacity of distribution transformers, leading to potential damage or failure, especially during peak hours like nighttime when many return home to charge their vehicles.
Innovation Solution
A load manager application that utilizes telematics data from electric vehicles and utility data to optimize charging schedules, coordinating the charging of EVs to avoid overloading distribution transformers, by delaying or reducing charging rates, and leveraging time-of-use discounts to spread the load across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple EVs charge simultaneously at customer locations, then EV users' charging needs are met, but distribution transformers become overloaded and may fail
Solution Approach 1:
The patent implements dynamic charging rate adjustment based on real-time transformer load conditions. The load manager continuously monitors transformer utilization and dynamically modifies EV charging rates, increasing them when transformer capacity is available and reducing them when approaching capacity limits, thereby resolving the contradiction between meeting charging demand and preventing overload
Solution Approach 2:
The system performs preliminary actions by scheduling EV charging in advance based on predicted transformer capacity and EV needs. The load manager analyzes historical data and forecasts to pre-coordinate charging schedules that optimize transformer utilization while ensuring EVs are charged by their required times, preventing last-minute overload situations
2Reliability
If EV charging is coordinated to avoid transformer overload, then transformer reliability is maintained, but charging time is extended
Solution Approach 1:
The system dynamically adjusts charging rates in real-time based on transformer load conditions. When transformer capacity is available, charging rates are maximized to reduce charging time. When approaching capacity limits, rates are temporarily reduced to maintain reliability, then increased again when capacity becomes available, optimizing both time and reliability
Solution Approach 2:
The load manager implements periodic monitoring and adjustment of charging schedules, reviewing transformer load conditions at regular intervals and making coordinated adjustments to EV charging rates. This periodic coordination allows the system to maintain reliability while minimizing total charging time through strategic rate variations
3Productivity
If charging rates are increased to meet EV demand quickly, then charging productivity is improved, but transformer load exceeds capacity causing damage
Solution Approach 1:
The patent implements a feedback mechanism where the load manager continuously monitors transformer load conditions and uses this information to adjust EV charging rates. When transformer utilization approaches dangerous levels, the system receives feedback and automatically reduces charging rates to prevent damage, while still maintaining high productivity when transformer capacity is sufficient
Solution Approach 2:
The system takes preliminary anti-action by proactively reducing charging rates before transformer damage can occur. The load manager monitors transformer conditions and preemptively adjusts charging schedules to prevent overload situations, addressing the harmful effect before it manifests as actual damage
Data Source
AI summary
Techniques are presented for the detection of whether an EV is using a household or other user location for charging. A machine learning model is trained on a training population of user locations using historical usage data and a label for a some of the user locations, typically a small proportion, indicating that an EV charges there, where the labels can, for example, be provided by a utility or derived from the EVs' telematics. The trained model can then be applied to un-labeled user locations' electricity usage data to detect EV charging, both assigning a label and a confidence value to the label. If telematics are available, for user locations at which EV charging is detected, the EV charging can be disaggregated from other electricity usage of the user location.