End User
EMOT
Description
This AI-driven Energy Management System (EMS) optimizes the charging and discharging of stationary batteries and electric vehicles to maximize on-site PV self-consumption in residential and community energy systems. By forecasting solar generation, household demand, and EV usage patterns, the service intelligently schedules energy flows to reduce grid dependency, avoid peak tariffs, and support local flexibility services. It considers dynamic constraints such as vehicle availability, user preferences, and real-time market signals, enabling the seamless integration of EVs as mobile storage units within the broader EMS strategy.
Core Capabilities
Predictive & Prescriptive Analytics
Optimization & Decision Support
Business Need
The rise of EV adoption and rooftop solar creates both opportunities and operational challenges for energy communities. Without intelligent coordination, excess PV is often exported at low value, while EV charging may contribute to costly peak loads. This service addresses the need for unified EMS strategies that treat EVs and batteries as coordinated flexibility assets. It enables households and communities to maximize self-consumption, reduce energy bills, and prepare for participation in local energy markets and grid services—while respecting EV driver mobility requirements.
Key Performance Indicators
PV self-consumption ratio
Battery round-trip efficiency
Grid import/export reduction
SOC trajectory stability
Forecast-optimization latency
Data Provided
PV generation forecasts (open weather APIs, system metadata)
Load forecasts from smart meter data (internal, household level)
Battery parameters (capacity, charge/discharge limits – internal)
Tariff data or market signals (optional, internal or open)
TEF
TEF EV