Grid-Asset Data Grows Even More Complex (And Even More Valuable) With EV Chargers In The Mix
By Satish Saini, HEXstream utilities industry specialist
Let’s talk electric vehicles, charging those EVs, and the infrastructure required to properly manage this increasing trend.
Most EV-charging infrastructure is connected to the utility grid, drawing power through grid assets. (The exception being stand-alone infrastructure). And any operational or performance issues with utility-grid assets directly affects EV-charging performance and efficiency. In a nutshell, when grids have problems, vehicles might not get charged.
The functionality of EV chargers is fully influenced by upstream grid-asset capacity, the operating conditions of those grid assets, and operational-performance parameters. The same is true in the opposite sense, too. EV-charger infrastructure, operations and functionality also directly affect utility grid assets.
So what does this mean? For one, there is a growing need for accurately mapping EV-charging infrastructure with connected grid assets. We need to understand what is connected, where it is connected and what and how it is working, etc. We also need to properly integrate the operational and performance data of grid assets (such as distribution transformers, feeders and substations) with the functioning data from EV chargers. This includes both real-time and historical data, along with event logs.
This is no small feat, particularly as more EVs are purchased and more charging infrastructure stations complicate these challenges.
The grid-asset data that is produced in this process—data that requires proper mapping, of course—includes:
- Asset type
- Asset location / ID
- Capacity
- Load patterns
- Phase unbalancing and fluctuations
- Voltage, sags and spikes
- Frequency, dips and fluctuations
- Outages and interruptions, including momentary interruptions
On the other hand, key EV-charger metrics that are Influenced by utility-asset performance can be defined as:
- Charger location and mapping-to-grid assets
- Charger availability, utilization and uptime
- Charging sessions over time (daily, weekly, etc.)
- Energy/kWh dispensed
- Power drawn, including maximum / peak power
- Charger-session failure rate
- Charger efficiency
- Fault rate
- Customer experience
Smarter grid-asset management=better grid-investment planning
Utilities that have effective asset-management strategies reliant on health / performance insights gleaned via operational-data analytics can approach EV infrastructure much more wisely. These utilities can plan, prioritize, install, operate and manage EV chargers with enhanced efficiency and lower charger-downtime. They can similarly manage their grid assets more effectively while providing key decision-making resources for grid-investment planning.
Simple? Not necessarily. But certainly achievable with the right commitment and solution provider. Some of the key elements to consider related to data integration / mapping with EV-charging assets include:
- Utility-grid schematics and single-line diagrams, along with major assets and corresponding EV chargers’ location maps.
- Key sources and platforms for data on grid assets, EV chargers and vehicles (including assets registry, assets operating data platforms, AMI / metering data, EV chargers’ data as defined above, third party / external data platforms for vehicle and chargers’ data).
- Data-acquisition from all sources, along with the mode of communication, including both real time and historical data.
- Advanced analytics and AI / ML applications for assets-loading (mapped with EV chargers’ operation), assets behavior and charging patterns, overloading / predictive maintenance and failure forecasts, optimization algorithms for grid assets and EV charger infrastructure, opportunities for demand response, tiered pricing, peak-load management and vehicle-to-grid energy.
- Alerts and notifications on key issues including failure predictions and overloading for effective decision-making with assets and chargers’ operation, including short-term and long-term grid-investment planning.
