Simulation of the GHG Abatement Potentials in the U.S. Building Sector by 2050

Michael Stadler, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 90R4000,

Berkeley, CA 94720, USA, &

Center for Energy and Innovative Technologies, Austria,

Chris Marnay, Lawrence Berkeley National Laboratory,

Nicholas Deforest,Lawrence Berkeley National Laboratory,

Florence Bonnet, Ecole des Mines de Nantes, France,

Judy Lai, Lawrence Berkeley National Laboratory,

Trucy Phan, Lawrence Berkeley National Laboratory,

Overview

It is obvious that climate change can be stabilized only by considering innovative building approaches and consumer behavior and bringing new, effective low-carbon technologies to the building/consumer market. This is the reason why Lawrence Berkeley National Laboratory (LBNL), U.S.A has been working for more than three years on an open source, long range Stochastic Lite Building Module (SLBM) to estimate the impact of different policies and consumer behaviors on the market penetration of low-carbon building technologies. The SLBM is part of the Stochastic Energy Deployment System (SEDS) project, which is used to track the performance of different U.S. Department of Energy (USDOE) Research and Development (R&D) activities on technology adoption, energy efficiency and CO2 reductions. SEDS explicitly incorporates uncertainty and is a multi-laboratory effort between the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Argonne National Laboratory (ANL), Lawrence Berkeley National Laboratory (LBNL), and private companies. The tool is fundamentally an engineering-economic model with technology adoption based on cost and energy performance characteristics of competing technologies. It also incorporates consumer preferences and passive building systems as well as interactions between building technologies. Furthermore, enduses are described by service demand, e.g. luminous intensity, which enables radical rethinking of how service needs are met. Several modules are already completed, e.g. PV or Lighting, and show extremely uncertain outcomes in 2050 depending on R&D levels and policies. The new window and building shell module of SLBM takes into account the cost savings for heating, cooling, and lighting due to different building shell/window performance options and compares them in a multi-attribute logit function with the investment costs. The core objective of this paper is to report on this new window and building shell module of SLBM and to show policy implications.

Methods

The work presented in this paper is part of the ongoing development of the Stochastic Energy Deployment System (SEDS), which follows a long history of modeling in support of planning and budgetary activities at the U.S. Department of Energy (USDOE). SEDS was commissioned to better support management, research direction, and budgetary decision-making for future R&D efforts. Specifically, it will be used to comply with the Government Performance Results Act of 1993 (GPRA), which requires federal government agencies, including USDOE, to predict and track the results of their programs and report them as a part of their obligations to the U.S. Congress. SEDS is currently used as a test and comparison tool for the fiscal year 2012 GPRA process.

The architecture of SEDS is that all energy producing and consuming activities in the US economy are modeled using a set of interconnected modules representing the key sectors, where the inputs to one module are the outputs from others (SEDS, 2009). The following modules constitute SEDS: Macroeconomic, Electricity Sector, Liquid Fuels, Buildings, Industry, Light Duty Vehicles, Heavy Duty Vehicles, Oil, Biomass, Biofuels, Hydrogen, Coal, and Natural Gas. In this paperwe focus on the Buildings module of SEDS, which we call Stochastic Lite Building Module (SLBM). The residential and commercial sectors in the SLBM can be thought of as aseries of stock models running in parallel that track equipment characteristics and market share as time progresses. The stock of equipment required is determined by the overall demand for its services, e.g. lum∙h/a for lighting. At each time step, a series of calculations are performed that take as input macroeconomic data, e.g. GDP, population and fuel prices, and output estimates of fuel consumption requirements for provision of a set of building services, i.e. lighting, domestic hot water, ventilation, refrigeration, other loads, heating, and cooling. A detailed description about the principle function of SLBM can be found at Stadler, 2009. The important addition to SLBM is the new building shell and window module, which calculates, based on a multi-attribute logit function, the market share of new buildings and retrofitted buildings. Four different window options are available within SLBM; highly insulating, low insulating, dynamic, and combined windows with different U-values, solar heat gain coefficients, durabilities, and costs. Together with the three different building shell types, this translates into 12 combinations/options that the model can chose from. These different combinations are characterized by different costs and energy reductions due to changed U-values or infiltration. SLBM calculates the energy savings for heating, cooling, ventilation and daylighting based on these 12 options, combines it with the energy price forecasts, and calculates the energy cost savings for every option. Then, these energy cost savings are compared to the annualized investment costs of every option by using a multi-attribute logit function that also considers consumer preferences. Finally, fuel consumption is then offset by on-site generation, e.g. PV. If any of the macroeconomic or other inputs are based on a probabilistic distribution rather than scalar values, the model runs multiple times with Monte Carlo draws.

Results

We incorporate the results of expert elicitations performed by the USDOE on expected window and lighting technology as well as PV performance for different R&D funding levels. These expert elicitations constitute investment cost, efficiency distributions, etc. for every assessed technology and year. Based on these stochastic inputs, SLBM finds mean values, mid values, standard deviations, etc. for the technology penetration as well as CO2 emissions by 2050. Figure. 1 shows the extreme uncertainty in outputs, the red line represents the deterministic result based on scalar inputs and the blue line the possible outcome in stochastic mode.

Figure 1. Probability Density in 2050 for the CO2 Reductions in the Commercial Sector, Target USDOE Case for PV, Lighting, and Windows; SLBM Version 21. April 2010

Conclusions

SEDS is currently used for the fiscal year 2012 GPRA process and extensively tested and compared to other models, e.g. the National Energy Modeling System (NEMS), which is entirely based on deterministic values and not open source. USDOE is supporting the creation of an open source uncertainty based forecasting tool, SEDS, which allows interpretation of the assumptions and results. The buildings aspect of SEDS is a mixture of innovation and tradition. The goal is to represent decision-making such that active, passive, and on-site energy conversion options are evenhandedly considered in a way that might allow for radical rethinking of building design, and therefore, R&D objectives and investments.

References

GPRA. Government Performance and Results Act,

SEDS, 2009:

Stadler Michael, Chris Marnay, Inês Lima Azevedo, Ryoichi Komiyama, and Judy Lai: The Open Source Stochastic Building Simulation Tool SLBM and its Capabilities to Capture Uncertainty of Policymaking in the US Building Sector, 32nd IAEE International Conference, Energy, Economy, Environment: The Global View, June 21-24, 2009, Grand Hyatt Hotel, San Francisco, CA, USA, LBNL-1884E.