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An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications

Author

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  • Akinkunmi Adegbenro

    (Siemens Mobility Limited, Langley Park Way, Chippenham SN15 1GE, UK
    School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK)

  • Michael Short

    (School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK)

  • Claudio Angione

    (School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK)

Abstract

Heating, ventilating, and air-conditioning (HVAC) systems account for a large percentage of energy consumption in buildings. Implementation of efficient optimisation and control mechanisms has been identified as one crucial way to help reduce and shift HVAC systems’ energy consumption to both save economic costs and foster improved integration with renewables. This has led to the development of various control techniques, some of which have produced promising results. However, very few of these control mechanisms have fully considered important factors such as electricity time of use (TOU) price information, occupant thermal comfort, computational complexity, and nonlinear HVAC dynamics to design a demand response schema. In this paper, a novel two-stage integrated approach for such is proposed and evaluated. A model predictive control (MPC)-based optimiser for supervisory setpoint control is integrated with a digital parameter-adaptive controller for use in a demand response/demand management environment. The optimiser is designed to shift the heating load (and hence electrical load) to off-peak periods by minimising a trade-off between thermal comfort and electricity costs, generating a setpoint trajectory for the inner loop HVAC tracking controller. The tracking controller provides HVAC model information to the outer loop for calibration purposes. By way of calibrated simulations, it was found that significant energy saving and cost reduction could be achieved in comparison to a traditional on/off or variable HVAC control system with a fixed setpoint temperature.

Suggested Citation

  • Akinkunmi Adegbenro & Michael Short & Claudio Angione, 2021. "An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications," Energies, MDPI, vol. 14(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2078-:d:532630
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    References listed on IDEAS

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    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Costa, Andrea & Keane, Marcus M. & Torrens, J. Ignacio & Corry, Edward, 2013. "Building operation and energy performance: Monitoring, analysis and optimisation toolkit," Applied Energy, Elsevier, vol. 101(C), pages 310-316.
    3. Tracey Crosbie & Michael Short & Muneeb Dawood & Richard Charlesworth, 2017. "Demand response in blocks of buildings: opportunities and requirements," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 4(3), pages 271-281, March.
    4. Hae Jin Kang, 2017. "Development of an Nearly Zero Emission Building (nZEB) Life Cycle Cost Assessment Tool for Fast Decision Making in the Early Design Phase," Energies, MDPI, vol. 10(1), pages 1-21, January.
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    Cited by:

    1. Joanna Piotrowska-Woroniak & Tomasz Szul & Krzysztof Cieśliński & Jozef Krilek, 2022. "The Impact of Weather-Forecast-Based Regulation on Energy Savings for Heating in Multi-Family Buildings," Energies, MDPI, vol. 15(19), pages 1-30, October.
    2. Konrad Nering & Krzysztof Nering, 2021. "Validation of Modified Algebraic Model during Transitional Flow in HVAC Duct," Energies, MDPI, vol. 14(13), pages 1-20, July.
    3. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
    4. Anatolijs Borodinecs & Jurgis Zemitis & Arturs Palcikovskis, 2022. "HVAC System Control Solutions Based on Modern IT Technologies: A Review Article," Energies, MDPI, vol. 15(18), pages 1-22, September.
    5. Flavio Muñoz & Ramon Garcia-Hernandez & Jose Ruelas & Juan E. Palomares-Ruiz & Carlos Álvarez-Macías, 2022. "Optimal Operation for Reduced Energy Consumption of an Air Conditioning System Using Neural Inverse Optimal Control," Mathematics, MDPI, vol. 10(5), pages 1-15, February.
    6. Ali Hamza & Muhammad Uneeb & Iftikhar Ahmad & Komal Saleem & Zunaib Ali, 2023. "Variable Structure-Based Control for Dynamic Temperature Setpoint Regulation in Hospital Extreme Healthcare Zones," Energies, MDPI, vol. 16(10), pages 1-27, May.

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