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Images of particles made from a promising battery cathode material called NMC

Optimizing industrial use of renewable energy: Industrial design for responsive demand

2017
Precourt Institute for Energy

PI: Adam R. Brandt, Energy Resources Engineering: Co-PIs: John Weyant, Management Science and Engineering, and Lou Durlofsky, Energy Resources Engineering


 

Project Summary

In an electricity system dependent on resources that cannot be fully controlled—wind and solar power—how flexible can heavy industries be in their use of electricity? What changes might various industrial sectors make in their equipment design and processes to take advantage of electricity rates that reward flexibility? The researchers will consider the perspectives of industrial consumers and utilities or grid operators. 


 

Publications: 

1. Teichgraeber, H., Brandt, A.R., 2020. "Optimal design of an electricity-intensive facility subject to electricity price uncertainty: stochastic optimization and scenario reduction". Chemical Engineering Research and Design.Under Review

2. Teichgraeber, H., Brandt, A.R., 2020. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities." Manuscript in Preparation.

3. Teichgraeber, H., Kuepper, L.E. and Brandt, A.R., 2020. "Designing reliable future energy systems by iteratively including extreme periods in time-series aggregation." Manuscript in Preparation.

4. Kuepper, L.E., Teichgraeber, H., Baumgärtner, N., Bardow, A. and Brandt, A.R., 2020. "Wind data introduce error in time series reduction for capacity expansion modeling". Manuscript in Preparation.

5. Kuepper, L.E., Teichgraeber, H., and Brandt, A.R.,2020. "CapacityExpansion: A capacity expansion modeling framework in Julia." Journal of Open Source Software, Under Review.

6. Teichgraeber, H., Lindenmeyer, C.P., Baumgärtner, N., Kotzur, L., Stolten, D., Robinius, M., Bardow, A. and Brandt, A.R., 2020. Extreme events in time series aggregation: A case study for optimal residential energy supply systems. Applied Energy, Accepted for publication (available as arXiv preprint arXiv:2002.03059).

7. Teichgraeber, H. and Brandt, A.R., 2019. "Clustering methods to findrepresentative periods for the optimization of energy systems: An initial framework and comparison". Applied Energy, 239, pp.1283-1293

8. Teichgraeber, H., Kuepper, L.E. and Brandt, A.R., 2019. TimeSeriesClustering: An extensible framework in Julia. Journal of Open Source Software, 4(41), p.1573.