Overview

intEMT is a modular and flexible software-toolbox for intelligent energy management designed to model, simulate and optimize complex energy systems. Its five core libraries can be used standalone or in combination, enabling tailored solutions from component-level modelling to full-scale intelligent energy management.

Optimization of energy systems

The overall goal of intEMT is to support the analysis and optimization of energy systems and components. For this purpose, intEMT provides various modules that assist and simplify the different phases of optimization. The focus is on the simulation-based investigation of the energy system in order to enable non-invasive optimizations and studies.

© Christopher Lange / Fraunhofer IISB
Illustration of the typical process of an energy system optimization in four steps: data acquisition and analysis, development of operating strategies, simulation and optimization, and implementation phase.

The "optimization-cycle" start with the data acquisition and analysis. In this stage, all input data is prepared for the further steps and a systematic evaluation is performed.

Based on the data analyis, operational strategies will be developed in the next step. The purpose of these strategies is to calculate and provide setpoints for the components, while taking into account various optimization goals. There are two different levels of strategies:

  • Plant level: local operational strategies which ensure the reliable operation of the plants (including storage, if applicable) and provide an interface to energy management.
  • Energy management level: overall control and optimization of the entire energy system.

In the step simulation and optimization, a digital twin of the energy system is developed. The model is used for scenario-based investigations (e.g., energy supply scenarios) and for optimal dimensioning of the components. Note the feedback to the operating strategies: these are also consolidated with the help of the digital twin and adapted as necessary.

The last step, the implementation phase, involves developing the automation functions and implementing them in the real system. Here, too, there is feedback: on the one hand, back to the simulation, and on the other, back to the start, true to the motto “after optimization is before optimization.”