EV Charging Infrastructure Control System for Grid Stability
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Solution Overview
Problem
The rapid growth of electric vehicles poses a challenge for electrical grids, as they were not designed to handle the increased electrical load, leading to potential overloading and the need for costly infrastructure upgrades. Current methods lack efficient strategies to optimize EV charging infrastructure without compromising energy supply or incurring high costs.
Innovation Solution
A software solution utilizing advanced mathematical modeling, optimization, and simulation techniques, including machine learning and stochastic optimization, to configure and control EV charging infrastructure. This system optimally sizes EV charging stations and schedules their operation based on consumer behavior and energy needs, integrating with distributed energy resources like solar photovoltaics and energy storage to minimize costs and prevent overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If EV charging infrastructure is expanded to meet growing demand, then charging availability and user satisfaction improve, but electrical grid overload risk and infrastructure cost increase
Solution Approach 1:
The system dynamically adjusts charging rates based on real-time grid conditions, DER availability, and queue status. Charging stations transition between different operational modes (full charge, partial charge, standby) to adapt to changing conditions, preventing grid overload while maximizing charging availability.
Solution Approach 2:
The system pre-cools or pre-heats EV batteries before peak charging demand to reduce the immediate power required during charging. It also pre-schedules charging sessions during off-peak hours when grid conditions are more favorable, reducing peak load on the electrical infrastructure.
2Productivity
If EV charging infrastructure is expanded rapidly, then charging capacity increases, but infrastructure upgrade cost and investment risk increase
Solution Approach 1:
The system uses existing electrical infrastructure and distributed energy resources (solar panels, battery storage) already present at facilities to provide EV charging capacity. By leveraging existing assets rather than requiring complete new infrastructure installations, the system reduces capital expenditure and investment risk.
Solution Approach 2:
The charging infrastructure is designed to serve multiple functions: EV charging, grid energy storage, peak shaving, and renewable energy integration. This multi-functionality maximizes the utility of each infrastructure component, reducing the need for dedicated EV charging infrastructure and lowering overall investment costs.
3Speed
If charging rates are increased to meet demand, then charging speed improves, but electrical load and risk of overloading increase
Solution Approach 1:
The system implements periodic charging cycles with variable rates rather than continuous high-rate charging. Charging sessions are interrupted and resumed at different power levels based on grid conditions, DER availability, and battery state, achieving necessary charge levels without sustained high electrical load.
Solution Approach 2:
The system dynamically changes charging parameters (power rate, voltage, current) based on real-time conditions including grid load, battery temperature, and DER output. This allows optimization of charging speed while maintaining electrical load within safe operational limits.
Data Source
AI summary
A system for configuring an Electric Vehicle (EV) charging infrastructure, the apparatus comprising. The system receives user direction and inputs regarding a custom EV charging infrastructure, submits the data an optimal planning module which is configured for performing optimization based on objectives in view of a set of constraints; and generates a recommendation from the optimal planning module for a custom EV charging infrastructure based on charging needs and behavior of the consumers toward deferring upgrades in electric infrastructure without compromising energy required for electrical transportation. The system validates the results from the optimal planning module using discrete event-based simulator and generates charging schedules from the controlling module for custom EV charging based on information about EV data, distributed energy resource (DER), and the electric infrastructure without compromising energy required for electrical transportation and causing undue impact on the electrical infrastructure.