TY - GEN
T1 - Energy optimization in net-zero energy building clusters
AU - Odonkor, Philip
AU - Lewis, Kemper
AU - Wen, Jin
AU - Wu, Teresa
N1 - Publisher Copyright:
Copyright © 2014 by ASME.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Genetic algorithm
KW - Net-zero
KW - Pareto optimality
KW - Smart grid
UR - http://www.scopus.com/inward/record.url?scp=84926196767&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84926196767&partnerID=8YFLogxK
U2 - 10.1115/DETC2014-34970
DO - 10.1115/DETC2014-34970
M3 - Conference contribution
AN - SCOPUS:84926196767
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 40th Design Automation Conference
T2 - ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014
Y2 - 17 August 2014 through 20 August 2014
ER -