EVSE Power Reallocation at Charging Stations Near Capacity Limits
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Solution Overview
Problem
The increasing prevalence of electric vehicles (EVs) poses a strain on grid infrastructure, particularly during peak hours, leading to potential grid overload, voltage instability, and equipment damage, necessitating efficient energy demand management to ensure grid stability and minimize infrastructure upgrade costs.
Innovation Solution
A method and system that utilize a cloud controller and an edge controller to manage the charging of EVs at an electric vehicle charging station. The system determines if the total power consumption exceeds a certain percentage of the maximum power capacity and, if so, recalculates and reallocates power to each EVSE, implementing dynamic load management to balance loads across phases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If EV charging is allowed without power management during peak hours, then charging service availability is improved, but grid overload and voltage instability occur
Solution Approach 1:
The system dynamically adjusts the power allocation to each EVSE based on real-time total power consumption levels. When power consumption exceeds thresholds, the system automatically recalculates and reduces individual EVSE power limits, enabling adaptive response to grid conditions without manual intervention
Solution Approach 2:
The system continuously monitors total power consumption at the charging station and uses this feedback to trigger power reallocation. The cloud controller receives consumption data, determines when thresholds are exceeded, and sends updated power allocation instructions back to the edge controller, creating a closed-loop control system
2Extent of automation
If cloud controller manages all EVSE power allocation, then centralized control is achieved, but response time to power consumption changes increases
Solution Approach 1:
The control system is segmented into two levels: cloud controller for centralized decision-making and edge controller for local execution. The cloud controller handles high-level power allocation decisions based on consumption thresholds, while the edge controller immediately executes power reallocation to individual EVSEs, dividing responsibilities to optimize both control and speed
Solution Approach 2:
The edge controller acts as an intermediary between the cloud controller and the EVSEs. It receives power allocation instructions from the cloud controller and directly implements them at the charging equipment level, serving as a local mediator that enables faster response without requiring direct cloud-EVSE communication for each adjustment
3Speed
If power is reallocated dynamically at edge controller, then response speed is improved, but system complexity increases
Solution Approach 1:
The edge controller is designed to autonomously execute power reallocation based on simple rules received from the cloud controller. It automatically calculates new power limits for each EVSE when triggered, without requiring complex centralized computation for each adjustment, enabling self-service operation that reduces overall system complexity
Data Source
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AI summary
A method for charging electric vehicle (EV) at an electric vehicle charging station equipped with a number of electric vehicle supply equipments (EVSEs) is provided. Charging at least one EV by the EVSEs is performed under control of a cloud controller. Whether a total power consumption corresponding to the EVSEs is equal or larger than X% of a maximum power capacity is determined. X is a real number. When the determination is yes, amount of power for each EVSE is recalculated by an edge controller and the recalculated amount of power is assigned to each EVSE by the edge controller for charging the at least one EV by the EVSEs under control of the edge controller. Charging information of each EVSE is sent to the cloud controller by the edge controller when performing charging the at least one EV by the EVSEs under control of the edge controller.