EV Charging Server Forecasting Power Demand
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Solution Overview
Problem
Existing charging systems for electric vehicles do not effectively manage power demand in predetermined areas, leading to peak power consumption issues that are not adequately addressed by current technologies.
Innovation Solution
A charging system that includes a server device capable of communicating with electric vehicles to forecast power demand based on travel and charge information, and adjusts charging schedules to manage power consumption within predetermined areas, using a communication unit, demand forecast unit, and schedule management unit to issue charge schedule change requests to control devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If charging schedules are adjusted to suppress peak power consumption in homes, then local power management is improved, but area-wide power demand is not suppressed
Solution Approach 1:
The system segments power management into hierarchical levels: individual home charging control and area-wide power demand management. The server device divides the service area into multiple predetermined areas and manages each area independently, allowing localized peak suppression while coordinating area-wide power distribution to prevent aggregate demand issues.
Solution Approach 2:
The system implements feedback mechanisms where the server device receives charging information from electric vehicles, forecasts power demand based on this data, and adjusts charging schedules accordingly. The demand forecast unit continuously monitors and predicts power demand patterns, providing feedback to the schedule management unit for iterative optimization of charging schedules across multiple areas.
2Ease of operation
If charging is scheduled based on individual home needs, then local convenience is improved, but area-wide peak demand is not suppressed
Solution Approach 1:
The system merges individual home charging schedules with area-wide power management. The server device combines charging information from multiple electric vehicles in a predetermined area, aggregates power demand forecasts, and generates coordinated charging schedules that satisfy individual charging needs while suppressing area-wide peak demand through collective optimization.
Solution Approach 2:
The charging schedules are made dynamic rather than static. The schedule management unit generates charging schedules that can be adjusted based on real-time power demand forecasts and actual charging patterns. The system dynamically modifies charging timing and power allocation to balance individual convenience with area-wide load management.
Data Source
AI summary
A charging system includes central server capable of communicating with electric vehicles, and charging stations. Central server receives travel information on the electric vehicles and remaining charge information on rechargeable batteries from the electric vehicles, forecasts demanded power in a predetermined area, and issues a charge schedule change request to charge control devices included in the predetermined area. Charging stations change charge schedules for the rechargeable batteries mounted on the electric vehicles in accordance with the charge schedule change request.