Investigating the Electrical Demand-Side Management Potential of Industrial Steam Supply Systems Using Dynamic Simulation

Fabian Borst, Nina Strobel, Thomas Kohne and Matthias Weigold
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Fabian Borst: Institute of Production Management, Technology and Machine Tools, Technical University of Darmstadt, 64287 Darmstadt, Germany
Nina Strobel: Institute of Production Management, Technology and Machine Tools, Technical University of Darmstadt, 64287 Darmstadt, Germany
Thomas Kohne: Institute of Production Management, Technology and Machine Tools, Technical University of Darmstadt, 64287 Darmstadt, Germany
Matthias Weigold: Institute of Production Management, Technology and Machine Tools, Technical University of Darmstadt, 64287 Darmstadt, Germany

Energies, 2021, vol. 14, issue 6, 1-20

Abstract: The increasing share of volatile, renewable energies, such as wind and solar power, leads to challenges in the stabilization of power grids and requires more flexibility in future energy systems. This article addresses the flexibilization of the consumer side and presents a simulation-based method for the technical and economic investigation of energy flexibility measures in industrial steam supply systems. The marketing of three different energy-flexibility measures—bivalence, inherent energy storage and adjusting process parameters—both at the spot market and at the balancing power market, are investigated from a technical as well as an economic point of view. Furthermore, the simulation-based methodology also considers pressure and temperature fluctuation induced by energy-flexibility measures. First, different energy-flexibility measures for industrial steam supply systems are introduced. Then, the physical modeling of the steam generation, distribution, and consumption as well as measure-specific control strategies will be discussed. Finally, the methodology is applied to a steam supply system of a chemical company. It is shown that the investigated industrial steam supply system shows energy-flexibility potentials up to 10 MW at peak and an annual average of 5.6 MW, which highly depend on consumer behavior and flexibility requirements.

Keywords: demand-side management; power-to-heat; dynamic simulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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