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OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies

Author

Listed:
  • Blonsky, Michael
  • Maguire, Jeff
  • McKenna, Killian
  • Cutler, Dylan
  • Balamurugan, Sivasathya Pradha
  • Jin, Xin

Abstract

Electrification and the growth of distributed energy resources (DERs), including flexible loads, are changing the energy landscape of electric distribution systems and creating new challenges and opportunities for electric utilities. Changes in demand profiles require improvements in distribution system load models, which have not historically accounted for device controllability or impacts on customer comfort. Although building modeling research has focused on these features, there is a need to incorporate them into distribution load models that include DERs and can be used to study grid-interactive buildings. In this paper, we present the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) model. OCHRE is a controllable thermal-electric residential energy model that captures building thermal dynamics, integrates grid-dependent electrical behavior, contains models for common DERs and end-use loads, and simulates at a time resolution down to 1 minute. It includes models for space heaters, air conditioners, water heaters, electric vehicles, photovoltaics, and batteries that are externally controllable and integrated in a co-simulation framework. Using a proposed zero energy ready community in Colorado, we co-simulate a distribution grid and 498 all-electric homes with a diverse set of efficiency levels and equipment properties. We show that controllable devices can reduce peak demand within a neighborhood by up to 73% during a critical peak period without sacrificing occupant comfort. We also demonstrate the importance of modeling load diversity at a high time resolution when quantifying power and voltage fluctuations across a distribution system.

Suggested Citation

  • Blonsky, Michael & Maguire, Jeff & McKenna, Killian & Cutler, Dylan & Balamurugan, Sivasathya Pradha & Jin, Xin, 2021. "OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies," Applied Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:appene:v:290:y:2021:i:c:s0306261921002464
    DOI: 10.1016/j.apenergy.2021.116732
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    References listed on IDEAS

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    1. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    2. Taniguchi-Matsuoka, Ayako & Shimoda, Yoshiyuki & Sugiyama, Minami & Kurokawa, Yusuke & Matoba, Haruka & Yamasaki, Tomoya & Morikuni, Taro & Yamaguchi, Yohei, 2020. "Evaluating Japan’s national greenhouse gas reduction policy using a bottom-up residential end-use energy simulation model," Applied Energy, Elsevier, vol. 279(C).
    3. Good, Nicholas & Zhang, Lingxi & Navarro-Espinosa, Alejandro & Mancarella, Pierluigi, 2015. "High resolution modelling of multi-energy domestic demand profiles," Applied Energy, Elsevier, vol. 137(C), pages 193-210.
    4. Love, Jenny & Smith, Andrew Z.P. & Watson, Stephen & Oikonomou, Eleni & Summerfield, Alex & Gleeson, Colin & Biddulph, Phillip & Chiu, Lai Fong & Wingfield, Jez & Martin, Chris & Stone, Andy & Lowe, R, 2017. "The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial," Applied Energy, Elsevier, vol. 204(C), pages 332-342.
    5. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    6. Jin, Xin & Baker, Kyri & Christensen, Dane & Isley, Steven, 2017. "Foresee: A user-centric home energy management system for energy efficiency and demand response," Applied Energy, Elsevier, vol. 205(C), pages 1583-1595.
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    Cited by:

    1. Earle, Lieko & Maguire, Jeff & Munankarmi, Prateek & Roberts, David, 2023. "The impact of energy-efficiency upgrades and other distributed energy resources on a residential neighborhood-scale electrification retrofit," Applied Energy, Elsevier, vol. 329(C).
    2. Wang, Jing & Munankarmi, Prateek & Maguire, Jeff & Shi, Chengnan & Zuo, Wangda & Roberts, David & Jin, Xin, 2022. "Carbon emission responsive building control: A case study with an all-electric residential community in a cold climate," Applied Energy, Elsevier, vol. 314(C).
    3. Gabriel Gomez-Ruiz & Reyes Sanchez-Herrera & Jose M. Andujar & Juan Luis Rubio Sanchez, 2024. "Simulation-Based Education Tool for Understanding Thermostatically Controlled Loads," Sustainability, MDPI, vol. 16(3), pages 1-24, January.
    4. Blonsky, Michael & McKenna, Killian & Maguire, Jeff & Vincent, Tyrone, 2022. "Home energy management under realistic and uncertain conditions: A comparison of heuristic, deterministic, and stochastic control methods," Applied Energy, Elsevier, vol. 325(C).
    5. Munankarmi, Prateek & Maguire, Jeff & Balamurugan, Sivasathya Pradha & Blonsky, Michael & Roberts, David & Jin, Xin, 2021. "Community-scale interaction of energy efficiency and demand flexibility in residential buildings," Applied Energy, Elsevier, vol. 298(C).
    6. Amir Shahcheraghian & Hatef Madani & Adrian Ilinca, 2024. "From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices," Energies, MDPI, vol. 17(2), pages 1-45, January.
    7. Yamaguchi, Yohei & Shoda, Yuto & Yoshizawa, Shinya & Imai, Tatsuya & Perwez, Usama & Shimoda, Yoshiyuki & Hayashi, Yasuhiro, 2023. "Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework," Applied Energy, Elsevier, vol. 333(C).

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