CERC-BEE Impact Model

TitleCERC-BEE Impact Model
Publication TypeReport
Year of Publication2016
AuthorsRobert Nachtrieb, David Fridley, Wei Feng, Nihan Karali, Nina Khanna, Nan Zhou, Jimmy Tran, Carolyn Szum
Date Published12/2016
Abstract

Currently there exist few building energy consumption models to test the national impact of building energy efficiency technology. The number of such models that are publicly available models is even fewer. In order to predict correctly the effectiveness of policy concepts, this is a need for realistic models of technology adoption and regulation.

This document describes a mathematical framework to accomplish this, and as an example estimates the impact of the portfolio of Building Energy Efficiency technologies advanced by the first five program years of US-China Clean Energy Research Center (CERC-BEE 1.0).

The system dynamics simulation [1] framework here calculates national building energy consumption. While the framework could be applied to any country (e.g. US or China), this paper describes a calibrated to the Reinventing Fire China baseline scenario. The resolution of the framework extends to Climate Zone, Building Type, End Use, and End Use Technology Types. The framework includes sub-models that realistically describe technology adoption, new building construction and building retrofit, gross domestic product and population. The framework naturally incorporates the impact of regulation and commercialization impact on technology availability. Finally, the framework simulation computer code is human-readable and open-source.

The framework can give a calibrated understanding of: adoption rates of building efficiency technology based on total cost of ownership with respect to incumbent (competitor) technologies. Building on the framework described herein, and incorporating mathematical representations of policy concepts, the parameter space can be explored to maximize anticipated policy impact. Possible extensions include: understanding building retrofit rates based on decision factors; and code adoption rates; and recommended procedures to update learning (actual vs projected) and adjust policy to maximize adoption rates.

Notes

Draft - Intended for Public Distribution