Can an Energy Management System Enable a Truly Realistic V2G Use Case?
The energy system inside a modern home or building is no longer simple. Solar PV systems, home batteries, EV chargers, heat pumps, and dynamic electricity tariffs all interact — often in conflicting ways. Each device has its own constraints, priorities, and impact on comfort, cost, and grid stability.
Coordinating all of this efficiently, in real time, requires more than static schedules or rule-based automation. It requires intelligence that can adapt continuously to changing conditions.
Why Optimization Is Harder Than It Looks
Even without electric vehicles, achieving optimal energy performance is already a complex task. A system aiming to minimize costs and maximize self-consumption must constantly anticipate:
What will the baseload consumption look like today?
When will solar production peak — and how steep will the ramp be?
How are electricity prices evolving throughout the day?
Is it better to charge now, later, or not at all?
These decisions cannot be made once per day. They must be revisited continuously as forecasts, consumption patterns, and market signals change.
This is where Reduxi’s AI-based Energy Management System (EMS) comes into play. By combining real-time measurements with predictive models, the system continuously evaluates options and adapts energy flows throughout the day — without requiring constant user input.
V2G Is Already Technically Possible — But Not Trivial
Vehicle-to-Grid (V2G) adds an entirely new dimension.
Unlike stationary batteries, an electric vehicle is mobile. It is not always connected. Its availability varies. Its state of charge changes outside the building. And its primary function — mobility — must always be respected.
From a control perspective, this introduces additional questions:
When will the vehicle be plugged in?
How long will it remain connected?
How much energy can be discharged without compromising the next trip?
Can the EV be reliably used for peak shaving, self-consumption optimization, or grid support?
Reduxi EMS: V2G Control Is Already Supported
Importantly, Reduxi EMS already supports direct control of V2G-capable chargers and vehicles.
Today, Reduxi can:
communicate with V2G chargers such as the Ambibox,
charge and discharge the vehicle battery as needed,
integrate the EV as an active energy resource alongside PV and stationary batteries,
and optimize charging and discharging based on real-time system conditions.
In this setup, the EV is treated as another controllable storage asset — with its own constraints and priorities — fully integrated into the overall energy optimization logic.
The Real Challenge: Availability and Behavior
While bidirectional charging is technically feasible, the real challenge for a realistic V2G use case lies elsewhere: predicting availability.
A stationary battery is always present.
A car is not.
This raises the central question:
How can an EMS make smart decisions without constantly asking the user when the car will be home and how much energy it needs?
Learning Instead of Asking
This is where behavioral AI becomes essential.
Reduxi’s EMS already learns from real-world usage patterns: how energy is consumed, when devices are typically used, and how behavior evolves over time. This allows the system to adapt automatically, without relying on fixed assumptions or manual input.
[Inference]
Applied to V2G, the same learning mechanisms can be used to anticipate vehicle availability and typical state-of-charge needs based on historical behavior — such as arrival times, charging habits, and driving patterns — while always prioritizing mobility requirements.
This transforms the EV from an unpredictable element into a context-aware flexibility resource, used only when conditions allow and user needs are respected.
From Static Control to Adaptive Intelligence
A truly practical V2G use case cannot rely on static schedules or simple rules. It requires an EMS that can:
continuously learn,
update forecasts in real time,
react instantly to changing conditions,
and balance comfort, mobility, cost optimization, and grid impact.
This is not about controlling individual devices in isolation. It is about orchestrating the entire energy system intelligently.
Always Learning. Always Optimizing. Ready for V2G.
V2G is not just a hardware feature — it is a system-level challenge.
With existing support for V2G-capable chargers and vehicles, combined with AI-driven learning and real-time optimization, Reduxi’s EMS provides the foundation needed to make V2G technically feasible today ja operationally realistic tomorrow.
Always Learning. Always Optimizing. Ready for V2G.
V2G is not just a hardware feature — it is a system-level challenge.
With existing support for V2G-capable chargers and vehicles, combined with AI-driven learning and real-time optimization, Reduxi’s EMS provides the foundation needed to make V2G technically feasible today ja operationally realistic tomorrow.