eVolution Networks Smart Energy Solution

Key features

1. Real Time GIS (Geographic Information System) with AMI Data Integration

This feature combines mapping and asset management data (AM/FM/GIS) with real-time smart meter data (AMI) to create a live, object-oriented map of the low-voltage (LV) grid. It allows for detailed drill-down to the meter level, enabling swift analysis of abnormalities and faults. This is unique because it offers unprecedented, real-time visual insight into the grid's operational state down to individual customer connections.

2. Phase Identification & Balancing

The AI monitors load and voltage on three-phase feeders and confirms customer phase connections. It then uses algorithms to provide data-driven recommendations for optimal phase load balancing, which is crucial for efficient power delivery and preventing overload. This directly addresses efficiency and potential reliability issues at the distribution level.

3. Fault Detection/Prediction

This module provides real-time alerts on faults and their magnitude. Crucially, it also analyzes the source of the fault and utilizes AI to predict short to medium-term faults, allowing for proactive maintenance and minimizing downtime. The predictive capability sets it apart, moving beyond reactive fault management.

4. Demand Forecasting

The software forecasts individual and aggregated customer demand, as well as solar irradiance and PV panel active power generation. It extracts and visualizes key insights such as yearly, monthly, weekly, and daily trends, enabling better resource allocation and planning for variable renewable energy sources. This feature is vital for balancing supply and demand in a dynamic energy landscape.

5. Peak Load Shaving & Voltage Tuning

This feature actively minimizes system peak loads by activating or controlling customer Distributed Energy Resources (DERs) like AC units, electric water heaters, and EV chargers. It also monitors and controls optimal voltage levels to ensure better performance and higher billing accuracy. This directly contributes to cost savings and energy efficiency by optimizing consumption during high-demand periods.