Optimizing Building Energy Operations via Dynamic Zonal Temperature Settings
Publication: Journal of Energy Engineering
Volume 140, Issue 1
Abstract
Deregulation of the energy sector has created new markets for producers as well as opportunities for consumers to meet their needs in a more customized way. Yet, traditional building energy management systems operate statically by adjusting air or water flow in heating and cooling systems in response to predetermined triggers, in relation to large deviations in the zone temperature from the equipment’s set-point temperature. The writers provide decision support to managers of buildings through dynamic control of the installed equipment that seeks to minimize energy costs. Assuming that the building’s occupants have comfort preferences expressed by upper and lower limits for the temperature, the writers model the effect of active equipment control (through changes to either the set point or valve flow) on the zone temperature, taking into account the external temperature, solar gains, building’s shell, and internal loads. The energy required to change the zone temperature in each time period is then used to calculate the energy cost in the objective function of an optimization problem. By implementing the model for actual public buildings, the writers demonstrate the advantages of more active equipment-management in terms of lower costs and energy consumption.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The research leading to the results reported in this paper has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 260041 for the collaborative project “Energy Efficiency and Risk Management in Public Buildings” (EnRiMa). The Center for Energy and Innovative Technologies (CET) is supported by the Austrian Federal Ministry for Transport, Innovation, and Technology through the “Building of Tomorrow” program and by the Theodor Kery Foundation of the province of Burgenland. The cooperation of Centro de Adultos La Arboleya of Fundación Asturiana de Atención y Protección a Personas con Discapacidades y/o Dependencias (Siero, Asturias, Spain), Fachhochschule Burgenland’s Pinkafeld campus (Burgenland, Austria), and Fachhochschule Technikum Wien’s ENERGYbase facility (Vienna, Austria) has greatly enhanced the writers’ understanding of energy management at the building level. The writers are grateful for comments received from attendees of the Computational Management Science Conference in London, U.K. (April 18–20, 2012) and the XXV EURO Conference in Vilnius, Lithuania (July 8–11, 2012). The writers have benefited from the suggestions provided by the associate editor and two anonymous referees. Feedback from Angel Luis Alvarez Iglesias (HC Energía), Emilio L. Cano (Universidad Rey Juan Carlos), and Paula Rocha (University College London) has also helped to improve this paper. All remaining errors are the writers’ own.
References
Conejo, A., Carrión, M., and Morales, J. (2010). Decision making under uncertainty in electricity markets, Springer, Heidelberg, Germany.
German Institute for Standardization (DIN). (2003). “Heizkörper und konvektoren–Teil 1: Technische spezifikationen und anforderungen.”, Berlin (in German).
Engdahl, F., and Johansson, D. (2004). “Optimal supply air temperature with respect to energy use in a variable air volume system.” Energy Build., 36(3), 205–218.
Hobbs, B. (1995). “Optimization methods for electric utility resource planning.” Eur. J. Oper. Res., 83(1), 1–20.
Hyman, L. (2010). “Restructuring electricity policy and financial models.” Energy Econ., 32(4), 751–757.
King, D., and Morgan, M. (2007). “Adaptive-focused assessment of electric power microgrids.” J. Energy Eng., 150–164.
Kumbaroğlu, G., and Madlener, R. (2012). “Evaluation of economically optimal retrofit investment options for energy savings in buildings.” Energy Build., 49(1), 327–334.
Liang, Y., Levine, D., and Shen, Z.-J. (2012). “Thermostats for the smartgrid: Models, benchmarks, and insights.” Energy J., 33(4), 61–96.
Livengood, D., and Larson, R. (2009). “The energy box: Locally automated optimal control of residential electricity usage.” Service Sci., 1(1), 1–16.
Marnay, C., Venkataramanan, G., Stadler, M., Siddiqui, A., Firestone, R., and Chandran, B. (2008). “Optimal technology selection and operation of commercial-building microgrids.” IEEE Trans. Power Syst., 23(3), 975–982.
Platt, G., Li, J., Li, R., Poulton, G., James, G., and Wall, J. (2010). “Adaptive HVAC zone modeling for sustainable buildings.” Energy Build., 42(4), 412–421.
Pruitt, K., Braun, R., and Newman, A. (2013). “Evaluating shortfalls in mixed-integer programming approaches for the optimal design and dispatch of distributed generation systems.” Appl. Energy, 102(1), 386–398.
Siddiqui, A., Marnay, C., Firestone, R., and Zhou, N. (2007). “Distributed generation with heat recovery and storage.” J. Energy Eng., 181–210.
Stadler, M., et al. (2012). “Optimal planning and operation of smart grids with electric vehicle interconnection.” J. Energy Eng., 95–108.
Wilson, R. (2002). “Architecture of power markets.” Econometrica, 70(4), 1299–1340.
Xu, B., Fu, L., and Di, H. (2008). “Dynamic simulation of space heating systems with radiators controlled by TRVs in buildings.” Energy Build., 40(9), 1755–1764.
Information & Authors
Information
Published In
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Mar 15, 2013
Accepted: Jun 24, 2013
Published online: Jun 26, 2013
Published in print: Mar 1, 2014
Discussion open until: May 11, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.