The goal of reducing energy-related emissions presents industrial companies with the challenge of adapting the volatility of renewable energy sources to the energy requirements of production processes. On the one hand, this requires the establishment of technological flexibilities. On the other hand, it increases both the demands on energy management and the associated opportunities. Taking forecast fluctuations and planning uncertainties into account, this requires not only reliable, robust planning, but likewise appropriate regulation for implementing schedules.

With the goal of developing a predictive, holistic and reconfigurable control concept for industrial energy supply systems, EDCSproof aims to solve these challenges. For this purpose, a generic and modular modeling method was developed for the simultaneous creation of hierarchically interacting optimization levels - the foundation for the symbiosis of operational energy optimization and adaptive, model predictive control. The added value of the Energy Demand Control System (EDCS) lies not only in ensuring and optimizing intra-operational energy use, but also gives distributed energy systems the ability to act as predictable, flexible consumers in grid-connected energy systems. The performance of the EDCS was evaluated on a complex reference energy system, which was modeled according to the principle of a digital twin and optimized in real-time by optimization models of the EDCS.

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