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Open AccessArticle

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

1
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
2
Schatz Energy Research Center, Humboldt State University, Arcata, CA 95521, USA
3
Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Energies 2020, 13(12), 3093; https://doi.org/10.3390/en13123093
Received: 18 May 2020 / Revised: 6 June 2020 / Accepted: 9 June 2020 / Published: 15 June 2020
(This article belongs to the Special Issue Evaluation of Energy Efficiency and Flexibility in Smart Buildings)
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. View Full-Text
Keywords: demand flexibility; control system; optimization; resiliency; smart buildings; distributed energy resources; model predictive control demand flexibility; control system; optimization; resiliency; smart buildings; distributed energy resources; model predictive control
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MDPI and ACS Style

Krishnan Prakash, A.; Zhang, K.; Gupta, P.; Blum, D.; Marshall, M.; Fierro, G.; Alstone, P.; Zoellick, J.; Brown, R.; Pritoni, M. Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids. Energies 2020, 13, 3093.

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