With the increasing and uneven penetration of EVs on Low Voltage (LV) networks, a rigorous and robust grid situational awareness of EVs would greatly help devise more effective strategies to mitigate their impact.
In this talk, Eneida.io introduce a solution to DSOs identify, in real time, when and where in the grid topology a particular EV is charging using low cost, secure, smart sensors at the secondary substation, through a series of load disaggregation machine learning algorithms.
Attend this session to:
Learn how to assess and optimise impact on the grid for an unevenly clustered neighborhood adoption of EVs.
Shed light on concerns such as network planning, flexibility management, peak demand and capacity overload, voltage violations, harmonic distortion and technical losses, as well as reduced QoS and impact on asset lifespan.
See demonstrations of an app for DSOs; a Collaborative Energy IoT modular platform for monitoring and optimised operation of LV Networks.