Isolatable Microgrid EV Charging Under Transformer Capacity Limits
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Solution Overview
Problem
The existing electric power grid infrastructure faces challenges in efficiently and cost-effectively charging multiple electric vehicles (EVs) simultaneously due to power limitations and insufficient capacity in local transformers, especially during fast charging sessions, which can lead to insufficient power delivery and overloading of home electrical systems.
Innovation Solution
A method and apparatus that utilize a power system controller to determine a charging schedule for EVs based on power consumption information, alternative power resource availability, and transactive energy information, allowing for efficient charging by optimizing power distribution within an isolatable microgrid, including the use of Distributed Energy Resources (DERs) and renewable energy sources, and enabling communication networks to transmit charging instructions for optimal energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple EVs are charged simultaneously using existing grid infrastructure, then charging demand is met, but transformer capacity is exceeded and power delivery becomes insufficient
Solution Approach 1:
The system performs preliminary scheduling of EV charging sessions by determining optimal start times, end times, and power levels before charging begins. The scheduling system analyzes transformer capacity constraints and EV energy requirements in advance, creating a charging schedule that prevents overloading while meeting charging demands. This preliminary planning allows multiple EVs to be charged simultaneously without exceeding transformer capacity limits.
Solution Approach 2:
The charging system dynamically adjusts power delivery levels based on real-time transformer capacity availability and changing EV charging requirements. The scheduling system continuously monitors system state and modifies charging parameters (power levels, timing) to optimize utilization of available transformer capacity while preventing overload conditions. This dynamic adaptation enables flexible response to varying load conditions.
2Speed
If fast charging is provided to meet EV energy requirements, then charging speed increases, but transformer capacity is exceeded and system reliability decreases
Solution Approach 1:
The system changes charging parameters (power levels, voltage, current) dynamically based on transformer capacity constraints and EV battery requirements. The scheduling system determines optimal power levels that maximize charging speed while remaining within transformer capacity limits. By adjusting these parameters over time and across different charging sessions, the system achieves fast charging where possible while maintaining system reliability.
Solution Approach 2:
The system preliminarily determines appropriate charging speeds and power levels based on transformer capacity forecasts and EV energy requirements before charging begins. This advance planning allows the system to allocate maximum feasible charging power to each EV while ensuring the sum of all charging demands does not exceed transformer capacity, thereby maintaining reliability while achieving fast charging where possible.
3Device complexity
If AC charging is used with power limits, then system complexity is reduced, but charging speed becomes insufficient for fast charging requirements
Solution Approach 1:
The scheduling system provides a universal platform that manages both AC and DC charging operations through a single intelligent control architecture. This multi-functional system handles different charging types, transformer capacity management, and EV scheduling uniformly, avoiding the need for separate complex control systems for each charging type. The universal scheduler optimizes power allocation regardless of charging method, achieving fast charging capability through intelligent timing and power management rather than hardware complexity.
4Speed
If DC charging is implemented for fast charging capability, then charging speed increases, but communication and power control complexity increases
Solution Approach 1:
The system merges DC fast charging functionality with the existing AC charging management infrastructure by implementing a unified scheduling system. Rather than creating separate complex control systems for DC and AC charging, the invention combines both charging types under a single intelligent scheduling platform that manages power allocation, timing, and coordination. This merging approach enables fast charging capability while avoiding proportional increases in overall system complexity through shared control architecture.
Data Source
AI summary
Method and apparatus for charging a battery of an electric vehicle (EV) may determine a power charging schedule for charging the EV from a microgrid, based on charging preference information for the EV, and also power consumption information for devices on the microgrid and alternative power resource information indicating availability of electric power for supply to the microgrid from an alternative power resource on the microgrid received via a communication network. A charging instruction signal for charging the EV from the microgrid, according to the power charging schedule, may be transmitted over the communication network.