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Hermann, Julia ; Rusche, Simon ; Moder, Linda ; Weibelzahl, Martin:
Watt’s Next? Leveraging Process Flexibility for Power Cost Optimization.
In: Business & Information Systems Engineering.
Bd. 66
(2024)
.
- S. 541-563.
ISSN 1867-0202
DOI: https://doi.org/10.1007/s12599-024-00888-1
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Abstract
The transition from fossil fuels to renewable energy sources poses major challenges for balancing increasingly weather-dependent power supply and demand. Although demand-side energy flexibility, offered particularly by industrial companies, is seen as a promising and necessary approach to address these challenges and realize benefits for companies, its implementation is not yet common practice. Often facing highly complex process landscapes and operational systems, process mining provides significant potential to increase transparency of actual process flows and to discover or reflect existing dependencies and interrelationships of activities, instances or resources. It facilitates the implementation of energy flexibility measures and enables the realization of monetary benefits associated with flexible process operation. This paper contributes to the successful integration of energy flexibility into process operations by presenting a design science research artifact called PM4Flex. This is a prescriptive process monitoring approach that uses linear programming to generate recommendations for pending process flows optimized under fluctuating power prices by utilizing established energy flexibility measures. Thereby, event logs and corresponding company- as well as process-specific constraints are considered. PM4Flex is demonstrated and evaluated based on its implementation as a software prototype, its application to exemplary data from two real-world processes exhibiting power cost savings of up to 75% compared to the original execution, and based on semi-structured expert interviews. PM4Flex provides new design knowledge at the interface of prescriptive process monitoring and the energy domain providing decision support to optimize industrial energy procurement costs.