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Thermal energy storage sizing for industrial waste-heat utilization in district heating: A model predictive control approach

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  • Knudsen, Brage Rugstad
  • Rohde, Daniel
  • Kauko, Hanne

Abstract

Thermal energy storage (TES) is a key technology for enabling increased utilization of industrial waste heat in district heating. The ability of TES to equalize offsets in demand and supply depends strongly on the sizing, control and integration in a heating plant. We consider the problem of sizing TES in heating plants utilizing a varying waste-heat source. To this end, we propose a combined dynamic simulation and model predictive control approach that accounts for the dynamics and optimal control of the heating plant with TES. A case study has been carried out on a district-heating plant located in Norway, with 90% of its annual heat production being heat recovered from the off-gas from a ferrosilicon plant. We evaluate the effective peak-heating reduction with different TES sizes and the energy-to-heat-flow-ratio for the TES discharging periods as performance metrics. For the case study, our results suggest that a modest TES tank volume of 1500 m3 is sufficient to achieve a half-year peak-heating reduction of 12% and comparable performance with larger volumes. The proposed methodology constitutes a numerically tractable means of incorporating the impact of model predictive control on the sizing of TES for heating plants with time-varying waste-heat supply and demand.

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  • Knudsen, Brage Rugstad & Rohde, Daniel & Kauko, Hanne, 2021. "Thermal energy storage sizing for industrial waste-heat utilization in district heating: A model predictive control approach," Energy, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221014481
    DOI: 10.1016/j.energy.2021.121200
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    Cited by:

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    6. Han, Gwangwoo & Joo, Hong-Jin & Lim, Hee-Won & An, Young-Sub & Lee, Wang-Je & Lee, Kyoung-Ho, 2023. "Data-driven heat pump operation strategy using rainbow deep reinforcement learning for significant reduction of electricity cost," Energy, Elsevier, vol. 270(C).
    7. Liu, Haoran & Yu, Jiaqi & Wang, Ruzhu, 2022. "Model predictive control of portable electronic devices under skin temperature constraints," Energy, Elsevier, vol. 260(C).
    8. Ziemele, Jelena & Dace, Elina, 2022. "An analytical framework for assessing the integration of the waste heat into a district heating system: Case of the city of Riga," Energy, Elsevier, vol. 254(PB).

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