Scheduling Strategy for Industrial Energy Flexibility and Self-consumption

The transition towards climate neutrality profoundly affects the industrial sector. In Germany, industry presently accounts for approximately 29 % of final energy consumption, primarily for process heat provision, with only a small fraction supplied by renewables. Industrial energy flexibility emerges as a key solution for integrating renewables, decarbonizing the industrial sector, and reducing production costs. This paper proposes an energy management strategy for scheduling industrial energy flexibility explicitly considering multiple energy carriers and prioritizing self-consumption. Results indicate that integrating self-consumption goals into the optimization process leads to significant improvements in CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions without imposing energy cost disadvantages.

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Published
2024-10-14
DOI
https://doi.org/10.1109/isgteurope62998.2024.10863374
Primary Topic
Scheduling and Optimization Algorithms
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article
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article

Scheduling Strategy for Industrial Energy Flexibility and Self-consumption

Alexander Sauer, Isabella Bianchini
Scheduling and Optimization Algorithms
article

Scheduling Strategy for Industrial Energy Flexibility and Self-consumption

Alexander Sauer, Isabella Bianchini
article en

Abstract

The transition towards climate neutrality profoundly affects the industrial sector. In Germany, industry presently accounts for approximately 29 % of final energy consumption, primarily for process heat provision, with only a small fraction supplied by renewables. Industrial energy flexibility emerges as a key solution for integrating renewables, decarbonizing the industrial sector, and reducing production costs. This paper proposes an energy management strategy for scheduling industrial energy flexibility explicitly considering multiple energy carriers and prioritizing self-consumption. Results indicate that integrating self-consumption goals into the optimization process leads to significant improvements in CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions without imposing energy cost disadvantages.

University of Stuttgart (DE), Fraunhofer Institute for Manufacturing Engineering and Automation (DE)
Affordable and clean energy
Openalex Percentile: Top 60%
Scheduling and Optimization Algorithms
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