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Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids

Author

Listed:
  • Anand Krishnan Prakash

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
    These authors contributed equally to this work.)

  • Kun Zhang

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
    These authors contributed equally to this work.)

  • Pranav Gupta

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • David Blum

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • Marc Marshall

    (Schatz Energy Research Center, Humboldt State University, Arcata, CA 95521, USA)

  • Gabe Fierro

    (Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94720, USA)

  • Peter Alstone

    (Schatz Energy Research Center, Humboldt State University, Arcata, CA 95521, USA)

  • James Zoellick

    (Schatz Energy Research Center, Humboldt State University, Arcata, CA 95521, USA)

  • Richard Brown

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • Marco Pritoni

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

Abstract

With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this paper, we present the “Solar+ Optimizer” (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area network connectivity. The software was evaluated in a real convenience store in northern California with on-site solar generation, battery storage and control of HVAC and commercial refrigeration loads. Preliminary tests showed price responsiveness of the building and cost savings of more than 10% in energy costs alone.

Suggested Citation

  • Anand Krishnan Prakash & Kun Zhang & Pranav Gupta & David Blum & Marc Marshall & Gabe Fierro & Peter Alstone & James Zoellick & Richard Brown & Marco Pritoni, 2020. "Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids," Energies, MDPI, vol. 13(12), pages 1-27, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3093-:d:371912
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    References listed on IDEAS

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    Cited by:

    1. Kyunghwan Choi & Dong Soo Kim & Seok-Kyoon Kim, 2020. "Disturbance Observer-Based Offset-Free Global Tracking Control for Input-Constrained LTI Systems with DC/DC Buck Converter Applications," Energies, MDPI, vol. 13(16), pages 1-18, August.
    2. Rovick Tarife & Yosuke Nakanishi & Yining Chen & Yicheng Zhou & Noel Estoperez & Anacita Tahud, 2022. "Optimization of Hybrid Renewable Energy Microgrid for Rural Agricultural Area in Southern Philippines," Energies, MDPI, vol. 15(6), pages 1-29, March.
    3. Touzani, Samir & Prakash, Anand Krishnan & Wang, Zhe & Agarwal, Shreya & Pritoni, Marco & Kiran, Mariam & Brown, Richard & Granderson, Jessica, 2021. "Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency," Applied Energy, Elsevier, vol. 304(C).

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