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.
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.