Research Papers: Design Automation

Adaptive Energy Optimization Toward Net-Zero Energy Building Clusters

[+] Author and Article Information
Philip Odonkor

Department of Mechanical
and Aerospace Engineering,
University at Buffalo—SUNY,
Buffalo, NY 14260
e-mail: podonkor@buffalo.edu

Kemper Lewis

Department of Mechanical
and Aerospace Engineering,
University at Buffalo—SUNY,
Buffalo, NY 14260
e-mail: kelewis@buffalo.edu

Jin Wen

Associate Professor
Civil, Architectural, and Environmental
Engineering Department,
Drexel University,
Philadelphia, PA 19104
e-mail: jinwen@drexel.edu

Teresa Wu

School of Computing, Informatics, Decision
Systems Engineering,
Arizona State University,
Tempe, AZ 85287-5906
e-mail: teresa.wu@asu.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 29, 2015; final manuscript received April 4, 2016; published online May 2, 2016. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 138(6), 061405 (May 02, 2016) (12 pages) Paper No: MD-15-1676; doi: 10.1115/1.4033395 History: Received September 29, 2015; Revised April 04, 2016

Traditionally viewed as mere energy consumers, buildings have adapted, capitalizing on smart grid technologies and distributed energy resources to efficiently use and trade energy, as evident in net-zero energy buildings (NZEBs). In this paper, we examine the opportunities presented by applying net-zero to building communities (clusters). This paper makes two main contributions: one, it presents a framework for generating Pareto optimal operational strategies for building clusters; two, it examines the energy tradeoffs resulting from adaptive decisions in response to dynamic operation conditions. Using a building cluster simulator, the proposed approach is shown to adaptively and significantly reduce total energy cost.

Copyright © 2016 by ASME
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Fig. 1

Overall schematic of NZEB cluster emulator [13]

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Fig. 2

Noncooling load profiles used for testing

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Fig. 3

Bilevel decision model for building cluster

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Fig. 7

System response to dynamic preferences

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Fig. 6

Energy cost profiles for dynamic energy pricing

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Fig. 5

Energy cost profiles for noncooling loads

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Fig. 4

Proposed decision framework

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Fig. 8

Temperature set-point response to customer preference

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Fig. 9

Pareto frontier of cost–cost tradeoff problem

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Fig. 10

Comparing Pareto solutions with previous work

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Fig. 11

Comparing total energy consumption between current model and results from Ref. [20]

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Fig. 12

Pareto band developed from 100 Monte Carlo simulations




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