Control active power feed-in intelligently

Forcast-based Charging strategy for battery storage systems

Do you wonder why, in the morning of a sunny day, your RCT Power storage system is already feeding solar power into the grid, although the battery has not yet been "fully" charged? There are two main reasons for this: the legally required active power reduction and the selected charging strategy of your battery storage system.

The RCT Power storage system has the forecast-based charging strategy as the factory default setting. It controls the active power reduction intelligently and in a way that serves the grid. You can, however, switch to a simplified charging strategy. Find out more about the exact background and the effects of the different charging strategies on this page.

Background: Active power reduction

Some of the subsidy programmes for battery storage systems mandate a limitation of the active power feed-in at the grid connection point. We refer to this as active power reduction.

The grid connection point constitutes the property boundary between the grid operator and the user/end customer. For private households, this is usually the house connection box. Only a defined percentage of the installed PV system power can be fed into the public grid from this grid connection point.

The German Renewable Energy Act (EEG), for example, states that the maximum active power feed-in must be limited to 70 per cent of the installed system capacity. For other use cases and to comply with the regulations of the subsidy programmes, the limits must be set to 60 per cent or even at 50 per cent.

The active power reduction can be implemented with an inverter setting. The setting ensures that not more than the maximum permitted percentage of the installed power is provided to the public grid. This task is nowadays often assumed by intelligent energy management systems. They intercept spikes in the power generation within the relevant setting and are often a basic requirement in subsidy programmes for battery storage systems.

Choosing the charging strategy

Two charging strategies are available for your RCT Power storage system:

  • Simplified charging strategy
  • Forecast-based charging strategy

The forecast-based charging strategy is the default for RCT Power storage systems. Deactivate this strategy in the battery settings if you want to use the simplified charging strategy.

 

Simplified charging strategy

The goal of this strategy is that the energy flow at the grid connection point approaches zero at all times during the day and the night. Ideally, no energy will be drawn from the grid, nor is any of the generated energy be fed into the grid.

The course of this charging strategy is summarised below:

  • The energy generated by the PV system is initially used to cover immediate self-consumption.
  • Excess energy is used to charge the battery.
  • Once self-consumption is covered and the battery storage is fully charged, any remaining excess energy is fed into the public grid, taking into account the active power reduction.
  • In the evening and night hours, the battery storage is discharged to cover self-consumption as far as possible.

Drawbacks of the simplified charging strategy:

The charging of the battery storage starts already in the morning hours, and a full charge of the battery is frequently achieved at midday. South-facing PV systems reach their peak output power at midday. If fully charged, the battery can no longer temporarily store the PV system output peaks. As a result, the solar power output must be controlled per the applicable specifications of the active power reduction. It leads to an inadequate and less efficient operation of the overall system, depending on the PV system and battery storage size and household energy consumption. 


Forecast-based charging strategy

The drawbacks of the simplified charging strategy can mainly be offset by a forecast-based charging strategy. The inverter has an integrated software programme that generates a yield forecast for the next few hours. This information is compared with a forecast of your anticipated energy consumption for the same time window. The storage system decides at which time charging the battery will provide the best possible yield, based on this calculation.

With the forecast-based charging strategy, there may be instances when solar energy is fed into the power grid in the morning, even though the battery storage has not been fully charged yet. Why does this happen? The forecast-based charging method delays the charging of the battery so that the PV system power output peak at midday can temporarily be stored in the battery. The stress on the power grid at midday is relieved, the self-consumption rate increases and the battery is preserved.

The following two scenarios will help you understand how the forecast-based charging strategy works (for a system with a control limit set at 70%):


The "Sunny Day" scenario

The forecast model expects a sunny day. In this scenario, the charging of the battery is delayed. The photovoltaic system feeds the energy it generates in the morning into the public grid. Only around noon, the charging of the battery starts. It is, therefore, able to absorb the power output peaks of the PV system and avoid active power reduction.  The point in time at which the battery is fully charged is reached much later. For solar energy that is fed into the grid in the morning, the feed-in tariff provides compensation and at midday, the active power reduction is avoided.

 

The "Rainy Day" scenario

The forecast model expects a rainy day. In this scenario, the system enters the earlier described "simplified charging strategy" with the first rays of sunlight. It will try and increase the self-consumption rate as no active power reduction of the PV system is expected in this scenario. It compensates for the household energy demand and charges the battery storage with the excess solar energy. Feeding energy into the public grid is thus avoided as far as possible.

 

RCT Power's forecast-based charging strategy is a self-learning system. It uses historical data to provide energy yield and load profiles with a view to the future. Actual irradiation and energy consumption are considered in addition. No internet connection to the weather forecast or comparable service providers is required. Immediately after commissioning, the system will have insufficient data to optimally execute the charging strategy. It requires a learning phase of approximately one week. During this time, it will learn more about user and location and can then take targeted decisions. 

 

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