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Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach

Author

Listed:
  • Michael Short

    (School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Cleveland TS1 3BA, UK)

  • Sergio Rodriguez

    (School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Cleveland TS1 3BA, UK)

  • Richard Charlesworth

    (Energy Management Division, Siemens plc, Princess Road, Manchester M20 2UR, UK)

  • Tracey Crosbie

    (School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Cleveland TS1 3BA, UK)

  • Nashwan Dawood

    (School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Cleveland TS1 3BA, UK)

Abstract

Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.

Suggested Citation

  • Michael Short & Sergio Rodriguez & Richard Charlesworth & Tracey Crosbie & Nashwan Dawood, 2019. "Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach," Energies, MDPI, vol. 12(22), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4320-:d:286372
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    References listed on IDEAS

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    1. 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.
    2. Djongyang, Noël & Tchinda, René & Njomo, Donatien, 2010. "Thermal comfort: A review paper," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2626-2640, December.
    3. Lei Zhou & Yang Li & Beibei Wang & Zhe Wang & Xiaoqing Hu, 2015. "Provision of Supplementary Load Frequency Control via Aggregation of Air Conditioning Loads," Energies, MDPI, vol. 8(12), pages 1-20, December.
    4. Short, Michael & Crosbie, Tracey & Dawood, Muneeb & Dawood, Nashwan, 2017. "Load forecasting and dispatch optimisation for decentralised co-generation plant with dual energy storage," Applied Energy, Elsevier, vol. 186(P3), pages 304-320.
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    Citations

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    Cited by:

    1. Davide Deltetto & Davide Coraci & Giuseppe Pinto & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings," Energies, MDPI, vol. 14(10), pages 1-25, May.
    2. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    3. Wieslaw Urban & Krzysztof Łukaszewicz & Elżbieta Krawczyk-Dembicka, 2020. "Application of Industry 4.0 to the Product Development Process in Project-Type Production," Energies, MDPI, vol. 13(21), pages 1-20, October.
    4. Davor Zoričić & Goran Knežević & Marija Miletić & Denis Dolinar & Danijela Miloš Sprčić, 2022. "Integrated Risk Analysis of Aggregators: Policy Implications for the Development of the Competitive Aggregator Industry," Energies, MDPI, vol. 15(14), pages 1-22, July.
    5. Sean Williams & Michael Short & Tracey Crosbie & Maryam Shadman-Pajouh, 2020. "A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services," Energies, MDPI, vol. 13(16), pages 1-30, August.
    6. Xiaoyi Zhang & Weijun Gao & Yanxue Li & Zixuan Wang & Yoshiaki Ushifusa & Yingjun Ruan, 2021. "Operational Performance and Load Flexibility Analysis of Japanese Zero Energy House," IJERPH, MDPI, vol. 18(13), pages 1-19, June.
    7. Rafał Trzaska & Adam Sulich & Michał Organa & Jerzy Niemczyk & Bartosz Jasiński, 2021. "Digitalization Business Strategies in Energy Sector: Solving Problems with Uncertainty under Industry 4.0 Conditions," Energies, MDPI, vol. 14(23), pages 1-21, November.

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