Charging Pole Load Scheduling for Grid Peak Reduction
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Solution Overview
Problem
The increasing penetration of electric vehicles poses challenges for electrical distribution grids due to varying charging demands, leading to potential energy price fluctuations and load peaks, necessitating efficient charging planning to minimize waiting time for drivers and optimize energy utilization for utility operators.
Innovation Solution
A method for planning electric vehicle charging involves receiving charging requests, determining and scheduling time slots based on energy demand and grid constraints, predicting load requirements, and optimizing these across interconnected charging poles to reduce peak loads and stabilize the grid, while also allowing for real-time adjustments and redirection of vehicles to optimize overall load management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If fast charging mechanisms are used to reduce charging time, then charging duration is improved, but grid load peaks and energy price fluctuations worsen
Solution Approach 1:
The system performs preliminary actions by predicting future load requirements and scheduling charging time slots in advance. The charging pole receives charging requests, determines energy demand, and schedules optimal charging time slots before actual charging occurs, preventing grid overload while ensuring fast charging when needed.
Solution Approach 2:
The system dynamically adjusts charging parameters based on real-time grid conditions. The charging pole communicates with the grid operator to receive feedback on current load status and dynamically modifies charging time slot allocations, transforming static grid infrastructure into a responsive system that balances fast charging needs with grid stability.
2Adaptability or versatility
If multiple vehicles charge simultaneously at distributed charging poles, then charging accessibility is improved, but grid load management becomes more complex
Solution Approach 1:
The system implements feedback mechanisms where charging poles communicate load requirements to the grid operator, who returns feedback on grid status and constraints. This closed-loop communication enables automated load management across multiple distributed charging poles, simplifying coordination while maintaining high charging accessibility.
Solution Approach 2:
The charging poles perform self-service by autonomously determining their own load requirements and scheduling charging time slots based on received requests and grid feedback. Each pole independently manages its charging schedule, reducing the complexity of centralized control while maintaining coordinated operation across the distributed network.
3Ease of operation
If charging time slots are optimized for individual vehicles, then driver satisfaction is improved, but overall grid load balancing deteriorates
Solution Approach 1:
The system merges individual vehicle charging requests into a collective load profile. The charging pole aggregates energy demands from multiple vehicles, determines optimal charging time slots that satisfy individual vehicle needs, and simultaneously balances overall grid load. This merging approach achieves both driver convenience and grid stability through unified scheduling.
Data Source
AI summary
A method for planning a charging of an electrical vehicle comprises receiving a request for charging from an electrical vehicle at a charging pole, determining a charging time slot for the electrical vehicle based on the request for charging, scheduling the charging time slot for the electrical vehicle, predicting a load requirement for the charging pole based on the request for charging and the charging time slot, and sending the load requirement to a grid operator supplying the charging pole with electrical power.