Battery scenario – with solar, dynamic contract, battery and EMS

Definition and context

The With solar + dynamic contract + battery + EMS scenario is the fifth and most advanced calculation shown in the annual savings chart.

This scenario combines a photovoltaic (PV) system, a dynamic electricity contract, a battery for energy storage, and an EMS controller for intelligent control. It builds directly on the With solar + dynamic contract + battery scenario, but adds active optimisation through EMS control. Instead of using the battery only to increase self-consumption, the system actively steers energy flows to maximise financial value.

 

What makes this scenario different

In the previous battery scenario, the battery is used passively. Solar surplus is stored and later used to avoid importing electricity from the grid.

In this scenario, the EMS controller actively manages energy flows. The system decides when to charge the battery from the grid, when to discharge the battery to cover consumption, and when exporting energy is financially attractive. This transforms the battery from a self-consumption tool into a financial optimisation asset.

 

How the scenario is calculated

Total savings are calculated by simulating active energy management over a full year.

Use of historical price data

The calculation uses dynamic electricity prices from the previous year. These prices are applied across the entire year to simulate realistic charging and discharging behaviour. This allows the system to identify recurring low-price and high-price periods.

 

Active energy management

Rather than passively responding to solar production or household demand, the EMS controller continuously evaluates electricity prices, the battery state of charge, and expected production and consumption. Based on this information, the system decides when to import electricity from the grid, when to charge or discharge the battery, and when to export energy to the grid.

All decisions are made to maximise financial outcome, within technical system limits and grid constraints.

 

Smart steering strategies

Two seasonal strategies clearly illustrate the added value of EMS control.

Winter strategy – arbitrage

During winter, solar production is typically insufficient to fully charge the battery. The EMS controller identifies moments when electricity prices are lowest and charges the battery from the grid during these low-price periods. The stored energy is then used during high-price hours.

The result is that expensive peak-hour grid imports are avoided by using cheaper energy purchased earlier.

Summer strategy – profit maximisation

During summer, solar production is high and consumption may be relatively low. The battery may not fully discharge overnight. The EMS controller identifies excess stored energy and exports it at the highest dynamic price of the day.

This ensures that surplus energy is monetised instead of remaining unused in the battery.

 

Goal and outcome

The purpose of this scenario is to demonstrate the additional value created by smart steering.

By comparing this scenario with With solar + dynamic contract + battery, users can clearly see the benefit of buying electricity at low prices, avoiding imports or selling energy at high prices, and the difference between passive storage and active optimisation. This scenario shows how EMS control enables true “buy low, use or sell high” behaviour.

 

System design benchmarking

In addition to the predefined scenarios, the annual savings chart often includes Your system design.

This benchmark compares the user’s current or proposed system design against the standard scenarios, from the current situation through to EMS. It provides insight into how well the system is optimised for savings and helps assess whether choices such as battery size, PV capacity, or the use of an EMS controller are financially effective.

 

Role in the annual savings analysis

In the annual savings chart, the With solar + dynamic contract + battery + EMS scenario represents the highest level of financial optimisation. It shows the full value of combining hardware with intelligent software and marks the transition from pure energy self-consumption to market-aware energy management.

It is the final step in the scenario comparison and illustrates what is achievable with a fully optimised system.