EV Charging Power Distribution Under Station and Grid Constraints
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Solution Overview
Problem
Power distribution systems face challenges in efficiently managing power distribution to electrical vehicles (EVs) during peak demand periods, particularly in overloaded systems with limited power capacity, and integrating renewable resources and vehicle-to-grid (V2G) power transmission complicates this further.
Innovation Solution
A method and system for controlling power distribution by determining EV power setpoints based on received data, updating and redetermining these setpoints to manage overload conditions, and controlling converters to balance power flow within the system, using a hybrid centralized-distributed architecture with distributed charging stations and a central controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple EVs are recharged simultaneously in a power distribution system, then the productivity and user satisfaction improve, but the system may experience overload conditions due to power limitations at charging equipment and grid interface
Solution Approach 1:
The system performs preliminary determination of EV power setpoints based on received data before actual charging begins. Overload conditions are predicted and prevented by calculating priority values and adjusting power distribution in advance, rather than reacting after overload occurs. This allows the system to prepare power allocation strategies proactively.
Solution Approach 2:
The system dynamically adjusts EV power setpoints based on real-time system conditions. When overload conditions are detected, the controller modifies power distribution by updating power setpoints according to calculated priorities, enabling flexible adaptation to changing load conditions while maintaining system reliability.
2Reliability
If power setpoints are adjusted to prevent overload conditions, then system reliability improves, but the time required for power distribution control and decision-making increases
Solution Approach 1:
Priority values are determined in advance based on system conditions and EV requirements. By pre-calculating priorities before overload occurs, the system establishes a ready-made allocation framework that enables rapid response when adjustments are needed, reducing decision-making time during critical moments.
Solution Approach 2:
The system continuously monitors power distribution conditions and uses feedback from overload detections to adjust power setpoints. This closed-loop control enables the system to learn from system responses and optimize adjustment timing, balancing reliability maintenance with minimal control intervention time.
3Reliability
If a centralized control architecture is used to manage power distribution, then system-wide coordination and reliability improve, but the device complexity and control computation requirements increase
Solution Approach 1:
The control system segments the power distribution problem into individual EV-level decisions. Each EV's power setpoint is determined independently based on its specific requirements and system conditions, rather than managing all EVs as a single complex entity. This segmentation simplifies the overall control architecture while maintaining system-wide coordination.
Solution Approach 2:
The system enables each EV to effectively manage its own power reception based on calculated priorities and setpoints. By distributing decision-making logic to individual EV control points rather than requiring centralized micromanagement of every parameter, the system reduces computational complexity while maintaining coordinated power distribution.
4Productivity
If power distribution is optimized for peak demand periods, then the productivity during critical times improves, but the loss of energy occurs due to power constraints and load management
Solution Approach 1:
The system changes power distribution parameters dynamically based on system conditions. By adjusting power setpoints and allocation strategies according to real-time measurements of load conditions, available capacity, and EV requirements, the system optimizes energy utilization during peak periods while minimizing waste through adaptive parameter modification.
Data Source
AI summary
The present disclosure relates to a method for controlling a power distribution system comprising distributed charging station(s) for coupling to electrical vehicle(s) (EVs) and a grid interface for coupling the distributed charging station(s) to a power grid. The method comprises determining an EV power setpoint of the EV(s) based on received data; determining a first overload condition based on the EV power setpoint and a first power constraint of the distributed charging station(s); updating the EV power setpoint based on the first overload condition; determining a second overload condition based on the updated EV power setpoint and a second power constraint of the grid interface; redetermining the EV power setpoint based on the updated EV power setpoint and the second overload condition; and controlling the power distribution system based on the redetermined EV power setpoint. The present disclosure also relates to a respective system and power distribution system.