NREL Teams with LBNL to Analyze PV Pricing Trends
September 10, 2013
The U.S. Department of Energy's (DOE)'s National Renewable Energy Laboratory (NREL) and Lawrence Berkeley National Laboratory (LBNL) have released the second edition of their jointly written briefing that provides a high-level overview of historical, recent, and projected near-term PV system pricing trends in the United States. This briefing draws on several ongoing research activities at LBNL and NREL:
- LBNL's annual Tracking the Sun report series, including Tracking the Sun VI (August 2013)
- NREL's bottom-up PV cost modeling
- NREL's synthesis of PV market data and projections.
The briefing, titled Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections (2013 Edition), examines progress in PV price reductions to help DOE and other PV stakeholders manage the transition to a market-driven PV industry. The briefing integrates different perspectives and methodologies for characterizing PV system pricing, in order to provide a broader perspective on underlying trends within the industry. By examining progress in PV price reductions, the briefing will also help DOE track progress toward the SunShot goals of reducing the cost of PV-generated electricity by about 75% between 2010 and 2020. The joint briefing indicates that the general downward trend in PV system pricing continued in 2012, and is expected to continue in 2013-14. However, the briefing also indicates that there is significant variation in reported pricing both geographically and across market segments.
The lead authors of the briefing, David Feldman (NREL) and Galen Barbose (LBNL), will be discussing the briefing at a webinar hosted by Vote Solar on September 11, 2013, from 10 A.M. – 11 A.M. PDT. You may register for the webinar through the following link: https://attendee.gotowebinar.com/register/6912211042462597376
The briefing was produced as part of an ongoing collaborative research effort between the two labs focused on solar technology system-level cost analysis and modeling. This research is supported by funding from the DOE's Solar Energy Technologies Program of the Office of Energy Efficiency and Renewable Energy.