Vehicle to grid (V2G) can be of great support to the UN sustainable development goal “affordable and clean energy“. In the near future, millions of electric vehicles (EV), that stay parked at home 95% of the time, form a gigantic battery which can store and release excess energy from the ever-fluctuating renewable energy sources.
V2G has been successfully demonstrated many times. New V2G chargers have recently been introduced. The new charging standard ISO15118 has been approved. Volkswagen has announced all it’s EV’s will be V2G capable in 2022. V2G is hot and happening, but implemented mainly in larger businesses.
Positive Design initiated the living lab “Vehicle to grid at home” (V2G @ home) in early 2021 to help home owners and small businesses play their part in the energy transition. To make V2G available for a broad public independent of EV vendor, EVSE vendor or power company. This is why it is based on open source software.
Of course, vehicle to grid (V2G) involves advanced technology and is complex. The aim of V2G @ home is to take this burden off the user’s shoulders by creating:
- An automated system optimising economic and ecological gains
- An easy setup without complex decisions
- A guaranteed minimum battery charge; the car can always be driven with at any time
- A “Charge Now!” button: quickly charge with max. power.
- A self-learning system that becomes better over time
What’s the logic here?
The logic is based on simple concepts with powerful logic.
Logic 1: Tune in on user
The system learns from the user behaviour, when does she/he use energy for the home, e.g. heating, cooking, washing, etc. When does she/he use the car for how long and how full is the battery on return.
Making sure the battery is filled optimally to suit the car usage.
Logic 2: Store and use own solar energy
This is a no-brainer: when there is excess solar energy, store it in the car battery for use when the sun is down. This is why the system makes accurate forecasts of solar production based on public weather forecasts. It also continuously monitors actual energy production to optimise on the go.
On average an EV can power a house for three days
Logic 3: Buy low, sell high
Another no-brainer: in case of dynamic tariffs one can charge the batteries when prices are low or even negative(!) and sell when prices are high. Public market prices are used for the coming 24 hours and forecasts are made up to 72 hours ahead.
Logic 4: Use greenest hour energy
The mix of electricity sources (coal, wind, gas, solar, etc.) differs per hour on the net. Based on this, the CO2 emissions/kWh differ over time. So one can optimise for an actual lower carbon footprint by using energy from the net mainly at the greener hours.
Each of the concepts stated above seems straightforward but already requires machine learning, e.g. for forecasting. Combing these use cases to achieve an optimal outcome becomes mind-bogglingly complex. The FlexMeasures platform is tailored for these kinds of tasks and quickly and efficiently calculates optimal charge/dis-charge schedules.