EV Charging Control System Grid Interaction Optimization
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Solution Overview
Problem
Current methods for controlling electric vehicle charging and discharging in power systems lack consideration for dynamic interactions between electric vehicles, generator sets, and the grid, leading to inefficiencies and potential battery damage due to over-charging or over-discharging.
Innovation Solution
A control system and method that includes a total control platform with a communication module, data storage and management module, dual-level optimization control module, and power distribution control module, which enables real-time interaction and optimization of electric vehicle charging and discharging to align with grid demands, avoiding battery damage and optimizing energy transmission costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If local control methods are used for electric vehicle charging and discharging, then implementation simplicity is improved, but system optimization and grid interaction capability deteriorate
Solution Approach 1:
The control system is divided into multiple hierarchical levels: upper-level optimization control module that performs global optimization considering grid conditions and lower-level execution modules that implement local control actions. This segmentation allows each level to focus on specific tasks, maintaining implementation simplicity while achieving comprehensive grid interaction capability through coordinated operation of different control levels.
2Reliability
If dynamic control of charging and discharging power is implemented, then battery life is improved, but control system complexity increases
Solution Approach 1:
The control system continuously monitors battery state of charge, power output, and grid conditions, using this feedback to dynamically adjust charging and discharging power. The optimization control module receives real-time data about battery status and grid demand, processes this information, and generates appropriate control commands that protect battery life while managing system complexity through automated decision-making algorithms.
3Loss of energy
If real-time optimization control is applied to charging and discharging, then energy transmission cost is reduced, but computational requirements and system complexity increase
Solution Approach 1:
The optimization control module performs preliminary calculations and decision-making about charging and discharging strategies based on predicted grid conditions and battery states. By pre-computing optimal power distribution plans and preparing control strategies in advance, the system reduces real-time computational requirements and energy transmission costs while managing complexity through proactive rather than reactive control.
Data Source
AI summary
A method for controlling charging and discharging of an electric vehicle, the method comprising: first, analyzing information such as the battery status and the history default rate of electric vehicles applying for joining a power-grid charging and discharging service, and screening out an electric vehicle that can participate in the charging and discharging service for electric vehicles in the future; then, determining an optimal combination state of a generator set and the electric vehicle by using a method of electric energy transmission cost comparison; and further monitoring in real time the status of the electric vehicle during charging and discharging, and performing real-time power control on the electric vehicle, whereby an electric vehicle aggregator not only can meet the requirements on the charging and discharging service of the power system, but also can implement energy management and real-time control of the electric vehicles during charging and discharging, thereby reducing the effect of charging and discharging on the vehicle-mounted power battery.