Edge Hardware Control for EV Charging Load and Tariff Response
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Solution Overview
Problem
The increasing adoption of electric vehicles requires a scalable infrastructure for managing charging stations, which is complicated by flexible and dynamic pricing factors, making it challenging to efficiently charge vehicles and optimize energy usage.
Innovation Solution
A scalable edge hardware system that communicates with a cloud environment to acquire optimization and load management set points, dispatching these through a local network to charging stations, and controlling charge and discharge parameters using energy-related inputs such as weather data, tariff information, and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a charging station system manages multiple vehicles with dynamic pricing factors, then charging optimization and load management improve, but system complexity increases
Solution Approach 1:
The system is divided into multiple independent charging stations, each with its own controller that can autonomously manage charging operations. This segmentation allows the overall system to handle multiple vehicles simultaneously while distributing computational complexity across individual units rather than requiring a centralized complex system.
Solution Approach 2:
The charging station system dynamically adjusts charging parameters based on real-time conditions including electricity pricing, demand signals, vehicle battery states, and load management requirements. This dynamic adaptability enables optimization without requiring static complex configurations, as the system automatically responds to changing conditions.
2Productivity
If charging parameters are adjusted based on flexible pricing factors, then charging cost-effectiveness improves, but control difficulty increases
Solution Approach 1:
The charging station controller autonomously determines optimal charging parameters by processing pricing signals, demand data, and vehicle requirements without requiring manual intervention. The system self-adjusts charging rates, timing, and power distribution based on economic and technical factors, eliminating the need for operators to manually manage complex pricing-based control decisions.
Solution Approach 2:
The system continuously monitors charging progress, electricity pricing changes, demand signals, and vehicle battery states, using this feedback to dynamically adjust charging parameters. This closed-loop control enables cost-effective charging operations by automatically responding to pricing fluctuations and load conditions without increasing operational complexity for users.
3Productivity
If real-time data communication is implemented between charging stations and cloud environment, then load management optimization improves, but communication infrastructure requirements increase
Solution Approach 1:
The charging station controllers are designed to perform multiple functions including local charging management, data collection, cloud communication, and autonomous decision-making. This multi-functionality allows the same hardware infrastructure to support both simple local operations and complex cloud-based load management without requiring separate dedicated communication systems.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for providing edge hardware system for distributed energy resources. One embodiment of a system includes a core device that is deployed in an edge environment of a site, the core device causing the edge hardware system to communicate with a cloud environment to acquire current optimization and load management set points for a charging station, dispatch the current optimization and load management set points through a local communications protocol via a local network to the charging station, and receive data from the charging station through the local network. In some embodiments, the core device causes the hardware system to communicate the data to the cloud environment via a wide area network and control charge and discharge parameters of an energy asset at the charging station using energy-related inputs.