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

Anand Krishnan Prakash, Kun Zhang, Pranav Gupta, David Blum, Marc Marshall, Gabe Fierro, Peter Alstone, James Zoellick, Richard Brown and Marco Pritoni
Additional contact information
Anand Krishnan Prakash: Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Kun Zhang: Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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

Energies, 2020, vol. 13, issue 12, 1-27

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.

Keywords: demand flexibility; control system; optimization; resiliency; smart buildings; distributed energy resources; model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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