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Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction

Author

Listed:
  • Ah-Yun Yoon

    (Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Seoul 08826, Korea)

  • Hyun-Koo Kang

    (Department of Electrical and Electronic Enginnering, Hannam University, 70 Hannam-ro, Daedeok-gu, Daejeon 34430, Korea)

  • Seung-II Moon

    (Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Seoul 08826, Korea)

Abstract

Electric utility companies (EUCs) play an intermediary role of retailers between wholesale market and end-users, maximizing their profits. Retail pricing can be well deployed with the support of EUCs to promote demand response (DR) programs for heating, ventilating, and air-conditioning (HVAC) systems in commercial buildings. This paper proposes a pricing strategy to help EUCs and building operators achieve an optimal DR of price-elastic HVAC systems, considering peak load reduction. The proposed strategy is implemented by adopting a bi-level decision model. The nonlinear thermal response of an experimental building room is modeled using piecewise linear equations, which helps convert the bi-level model to the single-level model. The pricing strategy is implemented considering a time-of-use (TOU) pricing scheme, leading to low price volatility. Case studies are conducted for two types of load curves and the results demonstrate that the proposed strategy helps EUC promote the price-based DR of the commercial buildings for conventional load curves. However, EUC cannot reduce the peak load on duck curve caused by the large introduction of photovoltaic generators, even with price-sensitive HVAC systems in commercial building. This will be addressed in future studies by inducing DR participation of HVAC systems in residential buildings.

Suggested Citation

  • Ah-Yun Yoon & Hyun-Koo Kang & Seung-II Moon, 2020. "Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction," Energies, MDPI, vol. 13(4), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:862-:d:321341
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    References listed on IDEAS

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    1. Mu-Gu Jeong & Seung-Il Moon & Pyeong-Ik Hwang, 2016. "Indirect Load Control for Energy Storage Systems Using Incentive Pricing under Time-of-Use Tariff," Energies, MDPI, vol. 9(7), pages 1-20, July.
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    Citations

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    Cited by:

    1. Zishan Guo & Zhenya Ji & Qi Wang, 2020. "Blockchain-Enabled Demand Response Scheme with Individualized Incentive Pricing Mode," Energies, MDPI, vol. 13(19), pages 1-17, October.
    2. 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.
    3. Anna Fensel & Juan Miguel Gómez Berbís, 2021. "Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(8), pages 1-2, April.
    4. Aneta Sapińska-Śliwa & Tomasz Sliwa & Kazimierz Twardowski & Krzysztof Szymski & Andrzej Gonet & Paweł Żuk, 2020. "Method of Averaging the Effective Thermal Conductivity Based on Thermal Response Tests of Borehole Heat Exchangers," Energies, MDPI, vol. 13(14), pages 1-20, July.
    5. Tamás Kis & András Kovács & Csaba Mészáros, 2021. "On Optimistic and Pessimistic Bilevel Optimization Models for Demand Response Management," Energies, MDPI, vol. 14(8), pages 1-22, April.
    6. Ghasem Ansari & Reza Keypour, 2023. "Optimizing the Performance of Commercial Demand Response Aggregator Using the Risk-Averse Function of Information-Gap Decision Theory," Sustainability, MDPI, vol. 15(7), pages 1-31, April.

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