期刊
ENERGY
卷 234, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.121200
关键词
Thermal energy storage; Waste-heat utilization; District heating; Model predictive control
资金
- HighEFF - Centre for an Energy Efficient and Competitive Industry for the Future under the FME-scheme (Centre for Environment-friendly Energy Research) [257632]
- LTTG thorn -Low-temperature thermal grids with surplus heat utilization (Knowledge Building Project) [280994]
- Research Council of Norway
- HighEFF
- LTTGthorn
Thermal energy storage (TES) plays a key role in increasing the utilization of industrial waste heat in district heating systems. Sizing, control, and integration of TES in a heating plant are crucial for equalizing demand and supply offsets. Dynamic simulation and model predictive control can effectively optimize the sizing of TES in heating plants utilizing a varying waste-heat source.
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 m(3) 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. (C) 2021 The Author(s). Published by Elsevier Ltd.
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