Short Definition
A forecast-based EMS uses forecasts of PV generation, energy consumption, electricity prices, and operating conditions to plan energy flows in advance. It can, for example, reserve BESS capacity for an expected load peak or use periods with lower electricity prices for charging.
Unlike purely reactive control, it considers not only current measurements but also expected future developments.
Forecast-Based EMS at a Glance
- Forecasts add a view of expected future conditions to current measurements.
- The EMS uses this information to create an operating plan for the BESS, PV system, and flexible loads.
- Technical limits, costs, and operational priorities are included in the planning process.
- Updated measurements and forecasts allow the operating plan to be recalculated regularly.
How Does a Forecast-Based EMS Work?
A forecast-based EMS combines system data, forecasting models, and optimisation logic. The result is a time-based operating plan that is implemented through setpoints and control commands.
1. Data Collection and Preparation
The calculation is typically based on:
- current and historical PV, load, and grid measurements
- BESS status data and technical operating limits
- weather data and weather forecasts
- electricity prices, tariffs, and external schedules
- production times and other operational planning data
The data must be correctly timestamped, checked for plausibility, and provided at a suitable resolution. Missing or inaccurate measurements can affect both the forecasts and the resulting operating plan.
2. Forecast Generation
Forecasting models calculate expected time series for PV generation, consumer load, electricity prices, or other relevant variables.
A PV forecast can consider weather forecasts, historical generation data, system parameters, and recent system performance. A load forecast can be based on historical consumption profiles, weekdays, time of day, production schedules, and other operational factors.
Depending on the application, statistical methods, physical models, Machine Learning (ML), or combinations of these approaches may be used. The forecast horizon can generally range from a few minutes to several days. The appropriate time resolution and horizon depend on the specific operating objective.
3. Operating Plan Calculation
The EMS uses the forecasts to plan the operation of controllable components. The calculation can take into account:
- BESS state of charge, capacity, and charging and discharging power
- efficiencies and permitted operating limits
- required power or energy reserves
- grid import and export limits
- electricity prices and external schedules
- load priorities and available flexibility
- BESS degradation or cycle-related costs, if modelled in the project
The optimisation logic evaluates possible operating patterns according to defined objectives. These may include reducing energy costs, limiting load peaks, increasing self-consumption, or providing flexibility.
A forecast-based EMS is not tied to a particular calculation method. Possible approaches include mathematical optimisation, model predictive control, and forecast-supported control rules.
4. Implementation of the Operating Plan
The operating plan is translated into specific setpoints or control commands. The EMS can, for example, define BESS charging and discharging power, shift flexible loads, or control PV output within permitted limits.
Actual implementation depends on the available interfaces, component response times, and the project-specific system architecture.
5. Continuous Updates
New measurements, updated forecasts, and changing system conditions can be incorporated into regular recalculations. In this rolling planning approach, only the next part of the operating plan is implemented before the EMS evaluates future operation again.
The recalculation frequency depends on factors such as system dynamics, data availability, the forecast horizon, and the specific application.
Typical Applications
Predictive Self-Consumption Optimisation
The EMS can reserve BESS capacity for an expected PV surplus. This prevents the BESS from becoming fully charged before the main period of PV generation.
Preparation for Load Peaks
If a high load is expected, the EMS can bring the BESS to a suitable state of charge in advance. During the peak, the BESS supplies power to limit grid import.
Price-Based BESS Operation
With time-variable electricity prices, the EMS can consider lower-price periods for charging and higher-price periods for discharging. A complete economic assessment must also account for efficiency losses, BESS degradation, and contractual conditions.
Compliance with Export Limits
If a PV surplus is expected, the EMS can make BESS capacity available or schedule flexible loads in advance. This can reduce the likelihood that PV output must be curtailed due to an export limitation.
Coordination of Multiple Operating Objectives
Self-consumption optimisation, peak shaving, price-based operation, and external schedules may compete for the same system resources. The EMS must prioritise these objectives and allocate the available power, capacity, and flexibility accordingly.
Benefits
A forecast-based EMS can:
- prepare the system for expected load and generation events
- use available BESS capacity more selectively
- coordinate PV, BESS, and flexible loads
- account for time-variable prices and external schedules
- balance multiple technical and economic operating objectives
The actual benefit depends on data quality, forecast accuracy, the system model, and the optimisation logic used.
Limitations and Technical Requirements
Forecast Uncertainty
A forecast is not a guaranteed prediction. Clouds, unexpected production changes, additional loads, or inaccurate input data can cause deviations. The EMS therefore requires sufficient reserves and defined fallback strategies.
Data Quality and System Model
The optimisation must represent the actual condition and technical characteristics of the system with sufficient accuracy. Unconsidered losses, power limits, response times, or degradation effects can result in unsuitable operating plans.
Competing Operating Objectives
Multiple applications may require the same BESS capacity at the same time. Priorities, reserves, and rules for resolving conflicts must therefore be clearly defined.
Economic Limitations
Even an accurate forecast does not guarantee an economic benefit. Prices, efficiency losses, degradation, grid-related costs, and contractual restrictions must be represented correctly and completely.
Local Control and Fallback Strategies
Predictive planning does not automatically replace fast system control. Where provided by the project architecture, a local control system monitors current measurements, enforces technical limits, and corrects short-term deviations.
If forecasts, communication links, or components become unavailable, the system must be able to switch to a safe fallback mode. Possible options include reactive control, fixed limits, or a predefined operating plan.
Difference Between Forecast-Based and Reactive Control
Reactive control primarily makes decisions based on the current system state. If grid import exceeds a defined limit, for example, the BESS begins to discharge.
A forecast-based EMS also considers the expected development of the system. It can therefore charge or discharge the BESS, or reserve capacity, before an anticipated event occurs.
In practice, both approaches can work together. Forecast-based optimisation creates the operating plan, while reactive control compensates for short-term deviations based on real-time measurements.
Implementation with EcoPhi
EcoPhi can consolidate measurements from PV systems, BESS, energy meters, and flexible loads and make them available for forecast-based operating planning.
Depending on the project configuration, weather-based or ML-supported PV forecasts and ML-based load forecasts can be generated, or external forecasts can be integrated. Operating plans may be calculated by EcoPhi or received from a connected optimisation system, depending on the system architecture.
EcoPhi can transmit the resulting setpoints to BESS, PV systems, and flexible loads through the available interfaces. Whether fast control functions and limit checks are executed locally depends on the selected hardware and project-specific architecture.
The functional scope, optimisation objectives, forecast horizon, and update intervals are defined according to the technical and economic requirements of each project.
Short Summary
A forecast-based EMS uses forecasts to plan the operation of PV systems, BESS, and flexible loads in advance. This allows systems to prepare for expected load peaks, PV surpluses, or price changes. Its effectiveness depends on forecast quality, an appropriate system model, and robust control and fallback strategies.
Frequently Asked Questions
What is the difference between a forecast-based EMS and a reactive EMS?
A reactive EMS responds primarily to current measurements, while a forecast-based EMS also considers expected future generation, consumption, prices, and operating conditions. In practice, predictive planning and reactive real-time control can be combined.
Which forecasts can a forecast-based EMS use?
Depending on the application, it can use forecasts for PV generation, energy consumption, electricity prices, production schedules, load peaks, and other operating conditions. The required forecasts depend on the system components and optimisation objectives.
Does a forecast-based EMS require Machine Learning?
No. Forecasts and operating plans can be generated using statistical methods, physical models, control rules, mathematical optimisation, ML, or combinations of these approaches.
What happens if a forecast is inaccurate?
The EMS can regularly update its operating plan using new measurements and revised forecasts. Reactive control, operating reserves, technical limit checks, and defined fallback modes can also help manage deviations.
Is a BESS required for forecast-based energy management?
No. Forecast-based planning can also be used to control flexible loads or PV output. However, a BESS provides additional flexibility because energy can be stored and used at a later time.