Optimized Cold Storage Energy Management - Miami and Los Angeles Case Study

Sebastian Thiem, Alexander Born, Vladimir Danov, Jochen Schäfer, Thomas Hamacher

Abstract

Smart management of cold thermal energy storages could help future sustainable energy systems drawing large shares of electricity from renewable sources to balance fluctuating generation. This paper introduces a model-based predictive control strategy for cold thermal energy storages. A novel ice storage model for simulating and optimizing partial charge and discharge storage operation is developed and validated. The optimization problem is solved using a Forward Dynamic Programming approach. A case study analysis for a very hot and humid location (Miami) and a rather temperate climate (Los Angeles) and for each four building types (apartment building, hospital, office, and school) reveals that total cost savings of up to 20% compared to conventional control strategies are possible.

References

  1. Bellman, R., 2003. Dynamic Programming. Dover Publications, Inc., Mineola, N.Y.
  2. Born, A., 2015. Modellierung und experimentelle Untersuchung einer Kompressionskälteanlage mit Eisspeicher für die optimale Einbindung in ein Smart Energy System. Technische Universität München.
  3. Braun, J.E., 2011. A Near-Optimal Control Strategy for Cool Storage Systems with Dynamic Electric Rates (RP-1252). HVAC&R Res. 13, 557-580.
  4. Claessen, F.N., Poutré, H. La, 2014. Towards a European Smart Energy System - ICT innovation goals and considerations. Brussels, Belgium.
  5. Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., Torcellini, P., Liu, B., Halverson, M., Winiarski, D., Rosenberg, M., Yazdanian, M., Huang, J., Crawley, D., 2011. U.S. Department of Energy Commercial Reference Building Models of the National Building Stock. Golden, Colorado.
  6. Florida Power & Light (FPL), 2015. Business rates, clauses and storm factors - Effective June 2015 [WWW Document]. URL https://www.fpl.com/rates/pdf/June2015_Business.pdf (accessed 1.6.15).
  7. Gebhardt, M., Kohl, H., Steinrötter, T., 2002. Preisatlas - Ableitung von Kostenfunktionen für Komponenten der rationellen Energienutzung. Duisburg-Rheinhausen.
  8. Henze, G.P., Dodier, R.H., Krarti, M., 1997. Development of a Predictive Optimal Controller for Thermal Energy Storage Systems. HVAC&R Res. 3, 233-264.
  9. Johansson, T.B., Patwardhan, A., Nakicenovic, N., Gomez-Echeverri, L., 2012. Global Energy Assessment -Toward a Sustainable Future, Global Energy Assessment (GEA). International Institute for Applied System Analysis (IIASA), Laxenburg, Austria.
  10. Lee, T.-S., Liao, K.-Y., Lu, W.-C., 2012. Evaluation of the suitability of empirically-based models for predicting energy performance of centrifugal water chillers with variable chilled water flow. Appl. Energy 93, 583-595. doi:10.1016/j.apenergy.2011.12.001.
  11. Lee, W.-S., Chen, Y. -Ting, Wu, T.-H., 2009. Optimization for ice-storage air-conditioning system using particle swarm algorithm. Appl. Energy 86, 1589-1595. doi:10.1016/j.apenergy.2008.12.025.
  12. Ma, Y., Borrelli, F., Hencey, B., Coffey, B., Bengea, S., Haves, P., 2012. Model Predictive Control for the Operation of Building Cooling Systems. IEEE Trans. Control Syst. Technol. 20, 796-803. doi:10.1109/TCST.2011.2124461.
  13. Southern California Edison, 2015. Schedule TOU-8-RTP - General Service - Large - Real Time Pricing. Rosemead, California.
  14. Thiem, S., Danov, V., Schaefer, J., Hamacher, T., 2015. Ice thermal energy storage (ITES) - Experimental investigation and modeling for integration into multi modal energy system (MMES), in: Proceedings of the 9th International Renewable Energy Storage Conference. Duesseldorf, Germany.
  15. U.S. Energy Information Administration, 2015. Annual Energy Outlook 2015 with projections to 2040. Washington, DC, USA.
  16. Wang, H., Chen, Q., 2014. Impact of climate change heating and cooling energy use in buildings in the United States. Energy Build. 82, 428-436. doi:10.1016/j.enbuild.2014.07.034.
  17. Wilden, M.W., Truman, C.R., 1985. Evaluation of Stratified Chilled-Water Storage Techniques - Volume 2: Appendixes. Albuquerque, New Mexico.
  18. Zhang, Y., Lu, N., 2013. Demand-side management of air conditioning cooling loads for intra-hour load balancing, in: 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT). IEEE, pp. 1-6. doi:10.1109/ISGT.2013.6497905.
Download


Paper Citation


in Harvard Style

Thiem S., Born A., Danov V., Schäfer J. and Hamacher T. (2016). Optimized Cold Storage Energy Management - Miami and Los Angeles Case Study . In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-184-7, pages 271-278. DOI: 10.5220/0005759602710278


in Bibtex Style

@conference{smartgreens16,
author={Sebastian Thiem and Alexander Born and Vladimir Danov and Jochen Schäfer and Thomas Hamacher},
title={Optimized Cold Storage Energy Management - Miami and Los Angeles Case Study},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={271-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005759602710278},
isbn={978-989-758-184-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Optimized Cold Storage Energy Management - Miami and Los Angeles Case Study
SN - 978-989-758-184-7
AU - Thiem S.
AU - Born A.
AU - Danov V.
AU - Schäfer J.
AU - Hamacher T.
PY - 2016
SP - 271
EP - 278
DO - 10.5220/0005759602710278