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Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance

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  • Andrew Izawa

    (Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA)

  • Matthias Fripp

    (Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA)

Abstract

A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather and occupancy. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants’ willingness to pay for thermal comfort with a bottom-up, nonlinear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a “brick wall” preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 1% reduction in costs vs. a similar “brick-wall” MPC approach with the same comfort and 6–11% reduction in costs vs. other control strategies in the literature. CCPSO can also be used to operate the building with much greater comfort and costs or much lower costs and comfort than the “brick-wall” approach, depending on user preferences. CCPSO also reduced peak-hours demand by 3% vs. the “brick-wall” strategy and 4–14% vs. other strategies. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% or more vs. other control methods. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours.

Suggested Citation

  • Andrew Izawa & Matthias Fripp, 2018. "Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance," Energies, MDPI, vol. 11(9), pages 1-18, September.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2373-:d:168646
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    References listed on IDEAS

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    1. Imelda & Matthias Fripp & Michael J. Roberts, 2018. "Variable Pricing and the Cost of Renewable Energy," NBER Working Papers 24712, National Bureau of Economic Research, Inc.
    2. Nezamoddini, Nasim & Wang, Yong, 2017. "Real-time electricity pricing for industrial customers: Survey and case studies in the United States," Applied Energy, Elsevier, vol. 195(C), pages 1023-1037.
    3. Li, Canbing & Shi, Haiqing & Cao, Yijia & Wang, Jianhui & Kuang, Yonghong & Tan, Yi & Wei, Jing, 2015. "Comprehensive review of renewable energy curtailment and avoidance: A specific example in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1067-1079.
    4. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    5. Imelda & Matthias Fripp & Michael J. Roberts, 2018. "Variable Pricing and the Cost of Renewable Energy," Working Papers 2018-2, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
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    Cited by:

    1. da Fonseca, André L.A. & Chvatal, Karin M.S. & Fernandes, Ricardo A.S., 2021. "Thermal comfort maintenance in demand response programs: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    2. Bandyopadhyay, Arkasama & Leibowicz, Benjamin D. & Webber, Michael E., 2021. "Solar panels and smart thermostats: The power duo of the residential sector?," Applied Energy, Elsevier, vol. 290(C).
    3. Kamal, Rajeev & Moloney, Francesca & Wickramaratne, Chatura & Narasimhan, Arunkumar & Goswami, D.Y., 2019. "Strategic control and cost optimization of thermal energy storage in buildings using EnergyPlus," Applied Energy, Elsevier, vol. 246(C), pages 77-90.
    4. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    5. Lorenzo Bartolucci & Stefano Cordiner & Vincenzo Mulone & Marina Santarelli, 2019. "Ancillary Services Provided by Hybrid Residential Renewable Energy Systems through Thermal and Electrochemical Storage Systems," Energies, MDPI, vol. 12(12), pages 1-18, June.

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