HARVEST THERMAL, INC. — National Science Foundation SBIR Phase II: EN

HARVEST THERMAL, INC. — SBIR Phase II award from National Science Foundation.

Amount
$1,000,000
Agency
National Science Foundation
Program / Phase
SBIR · Phase II
Topic
EN
Solicitation
NSF 21-565
NAICS
Place of performance
CA
Period
2022-05-01 → 2024-04-30

Description

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to enable a novel heating and hot water system that reduces energy use and emissions without compromising comfort. The replacement of gas/oil-fueled heating and hot water systems by efficient electric alternatives is critical to reduce (GHG) emissions from the building sector that accounts for nearly one-third of emissions worldwide. These reductions are achieved through electrification enhanced by “load shifting,” the practice of consuming energy from the grid at times when it is cheapest and cleanest, and storing that energy for use in the home all day (including peak usage times). The project goals are to improve the reliability, security, and scalable manufacturability of this system, making it affordable to install for most households. Residential-scale load shifting will help the housing industry move from fossil fuel-based heating to meet emissions reduction requirements in a cost-effective way. The load shifting capability using readily available hot water storage allows utilities to balance the energy grid and increase the deployment of renewable energy sources with reduced infrastructure costs, as well as reduce greenhouse gas (GHG) emissions.This SBIR Phase II project proposes to address the technical risks facing large scale deployment of heating and hot water load shifting to enable rapid deployment of high-efficiency heat pump systems for residential applications in the United States. The project will systematically build knowledge of system-level performance, reliability, and failure modes. Cost drivers in system integration and storage density will be addressed and improved. Secure cloud computing architecture and machine learning will address technical barriers to wide-spread optimization of loads. Taken together, the program supports the capability to deploy reliable, secure, tested hardware and software as a solution.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.