Forecast-based EMS vs. static EMS – why predictive energy management systems are increasingly defining the standard

The economic viability of battery storage in commercial and industrial applications today no longer depends primarily on the hardware, but to a decisive degree on the quality of the energy management system (EMS) deployed, since this determines when, how heavily and for what purpose a storage system is charged or discharged and thereby directly governs revenues, cost savings and aging effects.

Two fundamentally different approaches have become established in the market: the static EMS, which is based on fixed rules and historical assumptions, and the forecast-based EMS, which anticipates future load, generation and price signals and optimizes its decisions predictively.

1. What is a static EMS?

A static EMS follows predefined rules that typically rely on historical averages, fixed thresholds or simple heuristics, such as the rule of charging the storage system during the day from PV surplus and discharging it in the evening to increase self-consumption or for peak shaving.

These systems are comparatively easy to implement, require no complex forecasting models and behave transparently from the user's perspective, but they quickly reach their limits as soon as load profiles, electricity prices or regulatory conditions change or several use cases are to be served simultaneously.

Typical characteristics of static EMS are:

• Decisions based solely on the current system state

• No explicit consideration of future loads, generation or prices

• Limited adaptability under volatile conditions

• Fixed prioritization of individual use cases

The fixed prioritization of individual use cases frequently results in a static sizing and allocation of the storage system across individual operating strategies, so that the storage system is reserved throughout the entire year, for example 20% for self-consumption optimization and 80% for peak shaving, regardless of whether this allocation actually makes economic sense under changing load, price or generation conditions. A forecast-based EMS is required for a dynamic allocation of the storage system that responds, for example, to changing solar irradiation (winter vs. summer). 

2. What is a forecast-based EMS?

A forecast-based EMS extends this approach with an explicit look into the future by using forecasts for relevant influencing variables such as electrical load, PV generation, electricity prices or grid fees and deriving from them a temporally optimized operating mode for the storage system.

At the core of the system is an optimization model that, based on the forecasts, decides which charging and discharging power is economically optimal at which point in time, whereby technical constraints, degradation costs and several revenue sources can be taken into account simultaneously.

Characteristic features of forecast-based EMS are:

• Use of load, generation and price forecasts

• Time-coupled optimization over several hours or days

• Dynamic weighting and combination of several use cases

The forecasts for load, generation and price data are today produced using modern machine learning methods, with considerable advances in model architectures, data availability and computing power in recent years in particular leading to significant improvements in forecast accuracy, so that short-term and near-intraday predictions now achieve a quality that makes the economic deployment of forecast-based optimization methods practical and scalable in the first place. 

3. Economic differences in practical operation

The central advantage of a forecast-based EMS lies in its ability to resolve conflicts of objectives between different use cases, since, for example, aggressive peak shaving in the morning can prevent sufficient capacity from being available in the afternoon for high electricity prices or balancing energy.

Whereas a static EMS does not recognize such conflicts of objectives and potentially makes suboptimal decisions, a forecast-based EMS can deliberately forgo short-term savings in order to achieve higher overall revenues in the long term.

In practice, this frequently leads to:

• Better combinability of several use cases

• Additional revenues through market and trading applications

• Reduced battery aging through optimized cycle planning

• More stable results across different weather and price years

4. Robustness against uncertainty

A common argument against forecast-based systems is the inherent uncertainty of forecasts, particularly with volatile PV generation or short-term price movements.

Modern EMS, however, address this problem through regular re-optimization, conservative constraints and scenario analyses, so that forecast errors do not lead to systematic wrong decisions but merely fine-tune the optimal operating mode.

Static systems, on the other hand, do react robustly to forecast errors, but are structurally incapable of exploiting opportunities arising from price volatility or load shifting.

5. When does which approach make sense?

A static EMS can be a cost-effective entry-level solution in very simple applications with a clearly defined single use case and low volatility, for example with small PV storage systems focused purely on self-consumption optimization.

As soon as several revenue sources become relevant, however, a forecast-based EMS is effectively a prerequisite for exploiting the full economic potential of the storage system.

6. Conclusion

The comparison between forecast-based and static EMS is ultimately not a purely technical one, but an economic one, since the additional complexity of forecast-based systems is offset by significantly higher and more stable returns.

For commercial and industrial companies that understand battery storage as an active economic asset and not merely as a passive add-on system, a forecast-based EMS is therefore increasingly becoming the new standard.
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