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Energy Optimization in Net-Zero Energy Building Clusters

[+] Author Affiliations
Philip Odonkor, Kemper Lewis

University at Buffalo - SUNY, Buffalo, NY

Jin Wen

Drexel University, Philadelphia, PA

Teresa Wu

Arizona State University, Tempe, AZ

Paper No. DETC2014-34970, pp. V02AT03A023; 12 pages
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 40th Design Automation Conference
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4631-5
  • Copyright © 2014 by ASME


Traditionally viewed as mere energy consumers, buildings have in recent years adapted, capitalizing on smart grid technologies and distributed energy resources to not only efficiently use energy, but to also output energy. This has led to the development of net-zero energy buildings, a concept which encapsulates the synergy of energy efficient buildings, smart grids, and renewable energy utilization to reach a balanced energy budget over an annual cycle. This work looks to further expand on this idea, moving beyond just individual buildings and considering net-zero at a community scale. We hypothesize that applying net-zero concepts to building communities, also known as building clusters, instead of individual buildings will result in cost effective building systems which in turn will be resilient to power disruption. To this end, this paper develops an intelligent energy optimization algorithm for demand side energy management, taking into account a multitude of factors affecting cost including comfort, energy price, Heating, Ventilation, and Air Conditioning (HVAC) system, energy storage, weather, and on-site renewable resources. A bi-level operation decision framework is presented to study the energy tradeoffs within the building cluster, with individual building energy optimization on one level and an overall net-zero energy optimization handled on the next level. The experimental results demonstrate that the proposed approach is capable of significantly shifting demand, and when viable, reducing the total energy demand within net-zero building clusters. Furthermore, the optimization framework is capable of deriving Pareto solutions for the cluster which provide valuable insight for determining suitable energy strategies.

Copyright © 2014 by ASME
Topics: Optimization



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