Smart Electric Vehicle (EVs) Charging Network Management Using Machine Learning
The Energy Tech Hub and Centre for New Energy Technologies collaborated with a team of Omdena AI engineers to analyze EV charging data and applying machine learning for optimizing Electric Vehicle (EVs) Distributed Network Service planning.
The outputs from this project can help to derive insights on when and how much EVs get recharged as well as showing where the consumption peaks are. Knowing these charging patterns can inform the system operators to manage their networks and optimize the efficiency of EV charging operations.
The increasing uptake of electric vehicles and their charging pose challenges to today’s energy networks, e.g. unexpected peak load and voltage problems in the distribution network. There is a growing interest in understanding how and when EVs are charged to inform the design of charging incentives and energy management schemes. However, it is challenging to keep track of EV charging at a large scale in a cost-effective way. With smart meter data collected, this sheds light on using AI and machine learning techniques to detect EV charging from the meter data, providing an effective yet non-intrusive solution for Distributed Network Service Providers (DNSPs) to know how EVs are charged on their networks, which will inform their network planning, upgrade, and operations.
The project outcomes
Exploratory Data Analysis to understand consumption patterns
The overall method of the approach taken in this project has been as follows:
- Data Preparation: A good portion of the time was spent on data preparation to make a usable dataset for modeling.
- Exploratory Data Analysis (EDA): This step was focused on understanding consumption patterns for different types of users (EV, producers, peak consumers, etc).
The EDA process helped to derive insights on what information is most relevant for the modeling part.
AI modeling to detect EV charging patterns
The team implemented a machine learning clustering algorithm that groups similar timestamps of consumption in smart meters. Initially, the team chose three clusters. Meaning each of the time slots (for every smart meter) is grouped into three buckets (low, medium, high). Low indicates “low consumption”. Medium indicates medium-sized consumption and potentially non-EV appliances. High indicates potential EV charging.
The outputs from this project can help to derive insights on when and how much EVs get recharged as well as showing where the peaks are. Knowing these EV charging patterns can inform the system operators to manage their networks and optimize the efficiency of EV charging operations.
The model and insights were built into an Excel tool and dashboard.