Phan, Trucy | Engineering Papers

DOE OSTI2010

Storage Viability and Optimization Web Service

Non-residential sectors offer many promising applications for electrical storage (batteries) and photovoltaics (PVs). However, choosing and operating storage under complex tariff structures poses a daunting technical and economic problem that may discourage potential customers and result in lost carbon and economic savings. Equipment vendors are unlikely to provide adequate environmental analysis or unbiased economic results to potential clients, and are even less likely to completely describe the robustness of choices in the face of changing fuel prices and tariffs. Given these considerations, researchers at Lawrence Berkeley National Laboratory (LBNL) have designed the Storage Viability and Optimization Web Service (SVOW): a tool that helps building owners, operators and managers to decide if storage technologies and PVs merit deeper analysis. SVOW is an open access, web-based energy storage and PV analysis calculator, accessible by secure remote login. Upon first login, the user sees an overview of the parameters: load profile, tariff, technologies, and solar radiation location. Each parameter has a pull-down list of possible predefined inputs and users may upload their own as necessary. Since the non-residential sectors encompass a broad range of facilities with fundamentally different characteristics, the tool starts by asking the users to select a load profile from a limited cohort group of example facilities. The example facilities are categorized according to their North American Industry Classification System (NAICS) code. After the load profile selection, users select a predefined tariff or use the widget to create their own. The technologies and solar radiation menus operate in a similar fashion. After these four parameters have been inputted, the users have to select an optimization setting as well as an optimization objective. The analytic engine of SVOW is LBNL?s Distributed Energy Resources Customer Adoption Model (DER-CAM), which is a mixed-integer linear program (MILP) written and executed in the General Algebraic Modeling System (GAMS) optimization software. LBNL has released version 1.2.0.11 of SVOW. Information can be found at http://der.lbl.gov/microgrids-lbnl/current-project-storage-viability-website.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2010

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

Given the substantial contribution of the U.S. building sector to national carbon emissions, it is clear that to address properly the issue of climate change, one must first consider innovative approaches to understanding and encouraging the introduction of new, low-carbon technologies to both the commercial and residential building markets. This is the motivation behind the development of the Stochastic Lite Building Module (SLBM), a long range, open source model to forecast the impact of policy decisions and consumer behavior on the market penetration of both existing and emerging building technologies and the resulting carbon savings. The SLBM, developed at Lawrence Berkeley National Laboratory (LBNL), is part of the Stochastic Energy Deployment System (SEDS) project, a multi-laboratory effort undertaken in conjunction with the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Argonne National Laboratory (ANL) and private companies. The primary purpose of SEDS is to track the performance of different U.S. Department of Energy (USDOE) Research and Development (R&D) activities on technology adoption, overall energy efficiency, and CO{sub 2} reductions throughout the whole of the U.S. economy. The tool is fundamentally an engineering-economic model with a number of characteristics to distinguish it from existing energy forecasting models. SEDS has been written explicitly to incorporate uncertainty in its inputs leading to uncertainty bounds on the subsequent forecasts. It considers also passive building systems and their interactions with other building service enduses, including the cost savings for heating, cooling, and lighting due to different building shell/window options. Such savings can be compared with investments costs in order to model real-world consumer behavior and forecast adoption rates. The core objective of this paper is to report on the new window and shell features of SLBM and to show the implications of various USDOE research funding scenarios on the adoption of these and other building energy technologies. The results demonstrate that passive technologies contain significant potential for carbon reductions - exceeding 1165 Mt cumulative savings between 2005 and 2050 (with 50% likelihood) and outperforming similar R&D funding programs for distributed photovoltaics and high efficiency solid-state lighting.

25 ENERGY STORAGE↗