Efficient electricity consumption thanks to dynamic electricity tariffs and intelligent energy management - savings of 20%!
In December 2024, there was a so-called dark doldrums in Germany, which was accompanied by exceptionally high electricity prices. On December 4, the electricity price was 51.9 cents per kilowatt hour (kWh), while on the days of December 12 and 13, it peaked at up to 129 cents per kWh.
A dark doldrums refers to a weather situation in which both wind and solar power production are significantly reduced. This occurs when there is no wind for an extended period of time and at the same time the weather is overcast and cloudy, which significantly reduces the production of renewable energy. In such situations, the energy demand must be covered by other, often more expensive energy sources, which can lead to a significant increase in electricity prices.
Dark doldrums occur in Germany particularly in the winter months, as the sun shines less and the days are shorter. According to estimates, such weather conditions can last for several days to weeks and occur on average a few times a year. In view of the growing proportion of renewable energies in the German electricity mix, dark doldrums pose a particular challenge for energy security and price stability. Strategies such as the expansion of storage capacities, the diversification of the energy mix and the increased use of load management are essential to meet these challenges.
Even before the legal obligation from January 1, 2025, some electricity providers in Germany are already offering dynamic electricity tariffs. These tariffs offer consumers both opportunities and challenges and, if used cleverly, can help to improve electricity costs and grid stability.
What is the actual benefit of dynamic tariffs?
Under the challenging weather conditions in December - characterized by dark doldrums and high electricity prices - openEMS, as part of our energy management system, has so far enabled us to save around 20% on electricity costs compared to the base rate of 32.9 cents/kWh. If we take the average of all basic suppliers in Germany of 39 cents/kWh as a basis, the savings even amount to approx. 32%. But how exactly does this work?
Intelligent management through time-of-use controller
The system combines forecasts, market data and real-time calculations to optimize electricity consumption:
- Consumption forecast: The energy requirement for the coming period is calculated based on historical consumption data.
- PV yield forecast: The potential yield of the photovoltaic system is predicted using weather services.
- Price forecast: Electricity prices for the next 24 hours are retrieved and integrated directly from the electricity provider via an API.
A simulation is run every 15 minutes on the local control unit in the building (weEMS - Edge). All available data is taken into account in order to develop an optimized schedule for the energy flow.
The timetable includes the following optimizations:
- Self-optimization of the inverter: The aim is a zero feed-in at the grid transfer point, whereby the demand in the building is covered by the PV system or the storage system.
- Delayed battery charging: The battery is deliberately held back to avoid expensive mains power at peak times.
- Active grid charging: The battery is specifically charged from the grid when electricity prices are at their lowest and it makes sense based on the forecast data.
Example: December 13 - a day with peak prices
The attached graph shows the optimization for 13 December, one of the days with the highest electricity prices. In the morning, the battery was deliberately prevented from discharging in order to save the stored energy for more expensive hours. In addition, the battery was actively charged from the grid between 2 and 4 a.m., when prices were at their lowest.
Thanks to this intelligent management, the average electricity price for this extreme day was kept below the base price of 32.9 cents/kWh - despite the challenging conditions.
In addition to monitoring, control and the tenant electricity model, dynamic electricity tariffs offer a further opportunity to save costs and generate additional returns.
