A Model of U.S. Commercial Distributed Generation Adoption

Technical Report · Tue Jan 10 04:00:00 EST 2006

DOI:https://doi.org/10.2172/876212· OSTI ID:876212

LaCommare, Kristina Hamachi; Firestone, Ryan; Zhou, Nan; Maribu, Karl; Marnay, Chris

Small-scale (100 kW-5 MW) on-site distributed generation (DG) economically driven by combined heat and power (CHP) applications and, in some cases, reliability concerns will likely emerge as a common feature of commercial building energy systems over the next two decades. Forecasts of DG adoption published by the Energy Information Administration (EIA) in the Annual Energy Outlook (AEO) are made using the National Energy Modeling System (NEMS), which has a forecasting module that predicts the penetration of several possible commercial building DG technologies over the period 2005-2025. NEMS is also used for estimating the future benefits of Department of Energy research and development used in support of budget requests and management decisionmaking. The NEMS approach to modeling DG has some limitations, including constraints on the amount of DG allowed for retrofits to existing buildings and a small number of possible sizes for each DG technology. An alternative approach called Commercial Sector Model (ComSeM) is developed to improve the way in which DG adoption is modeled. The approach incorporates load shapes for specific end uses in specific building types in specific regions, e.g., cooling in hospitals in Atlanta or space heating in Chicago offices. The Distributed Energy Resources Customer Adoption Model (DER-CAM) uses these load profiles together with input cost and performance DG technology assumptions to model the potential DG adoption for four selected cities and two sizes of five building types in selected forecast years to 2022. The Distributed Energy Resources Market Diffusion Model (DER-MaDiM) is then used to then tailor the DER-CAM results to adoption projections for the entire U.S. commercial sector for all forecast years from 2007-2025. This process is conducted such that the structure of results are consistent with the structure of NEMS, and can be re-injected into NEMS that can then be used to integrate adoption results into a full forecast.

• Research Organization: Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
• Sponsoring Organization: USDOE. Assistant Secretary for Energy Efficiency andRenewable Energy. Office of the Deputy Assistant Secretary for TechnologyDevelopment. Office of Distributed Energy Program
• DOE Contract Number: AC02-05CH11231
• OSTI ID: 876212
• Report Number(s): LBNL--57962
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING, POLICY, AND ECONOMY
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
COMMERCIAL BUILDINGS
COMMERCIAL SECTOR
DIFFUSION
ENERGY SYSTEMS
FORECASTING
HOSPITALS
MANAGEMENT
MARKET
PERFORMANCE
RELIABILITY
SIMULATION
SPACE HEATING
US ENERGY INFORMATION ADMINISTRATION
distributed generation DG DER-CAM customer adoptionmodel