An Integrated Optimization Model for Industrial Energy System Retrofit with Process Scheduling, Heat Recovery, and Energy Supply System Synthesis

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Abstract

The urgent need for CO2 reduction is calling upon the industry to contribute. However, changes within local energy supply systems including efficiency enhancement are bound to several economical and technical constraints, which results in interfering trade-offs that make it difficult to find the optimal investment option for CO2 mitigation. In this article, a new optimization model is presented that allows to optimize the design and operation of a supply and heat recovery system and production scheduling simultaneously. The model was used for retrofitting of a small brewery’s local energy system to identify decarbonization measures for eight potential future scenarios with different technical, economical and ecological boundary conditions. The results show that the proposed cost-optimized changes to the current energy system only slightly reduce carbon emissions if decarbonization is not enforced since the optimal solutions prioritize integration of photo voltaic (PV) modules that mainly substitute electricity purchase from grid, which is already assumed to be carbon free. However, enforcing decarbonization rates of 50% for the assumed future boundary conditions still results in cost savings compared to the current energy system. These systems contain heat pumps, thermal energy storages, electric boilers, and PV. Battery storages are only part of the optimal system configuration if low to moderate decarbonization rates below 50% are enforced. An analysis of marginal costs for units not considered in the optimal solutions shows that solar thermal collectors only require small decreases in collector cost to be selected by the solver.

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Beck, A., Knöttner, S., Unterluggauer, J., Halmschlager, D., & Hofmann, R. (2022). An Integrated Optimization Model for Industrial Energy System Retrofit with Process Scheduling, Heat Recovery, and Energy Supply System Synthesis. Processes, 10(3). https://doi.org/10.3390/pr10030572

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