Dev Millstein

Tracking the Sun VIII The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States

Author(s): Barbose, Galen; Darghouth, Naim; Millstein, Dev; Cates, Sarah; DiSanti, Nicholas; Widiss, Rebecca | Abstract: Berkeley Lab’s Tracking the Sun report series is dedicated to summarizing trends in the installed price of grid-connected, residential and non-residential systems solar photovoltaic (PV) systems in the United States. The present report, the ninth edition in the series, focuses on systems installed through year-end 2015, with preliminary data for the first half of 2016. The report provides an overview of both long-term and more-recent trends, highlighting key drivers for installed price declines over different time horizons. The report also extensively characterizes the widespread variability in system pricing, comparing installed prices across states, market segments, installers, and various system and technology characteristics. The trends described in this report derive from project-level data collected by state agencies and utilities that administer PV incentive programs, solar renewable energy credit (SREC) registration systems, or interconnection processes. In total, data for this report were compiled and cleaned for more than 820,000 individual PV systems, though the analysis in the report is based on a subset of that sample, consisting of roughly 450,000 systems with available installed price data. For the first time, the full underlying dataset of project-level data (excluding any confidential information) is available in a public data file, for use by other researchers and analysts.

Tracking the Sun VIII

Funded by the SunShot Initiative, this report summarizes trends in the installed price of grid-connected residential and non-residential PV systems in the United States.

Mesoscale climatic simulation of surface air temperature cooling by highly reflective greenhouses in SE Spain.

A long-term local cooling trend in surface air temperature has been monitored at the largest concentration of reflective greenhouses in the world, at the Province of Almeria, SE Spain, associated with a dramatic increase in surface albedo in the area. The availability of reliable long-term climatic field data at this site offers a unique opportunity to test the skill of mesoscale meteorological models describing and predicting the impacts of land use change on local climate. Using the Weather Research and Forecast (WRF) mesoscale model, we have run a sensitivity experiment to simulate the impact of the observed surface albedo change on monthly and annual surface air temperatures. The model output showed a mean annual cooling of 0.25 °C associated with a 0.09 albedo increase, and a reduction of 22.8 W m(-2) of net incoming solar radiation at surface. Mean reduction of summer daily maximum temperatures was 0.49 °C, with the largest single-day decrease equal to 1.3 °C. WRF output was evaluated and compared with observations. A mean annual warm bias (MBE) of 0.42 °C was estimated. High correlation coefficients (R(2) > 0.9) were found between modeled and observed values. This study has particular interest in the assessment of the potential for urban temperature cooling by cool roofs deployment projects, as well as in the evaluation of mesoscale climatic models performance.

Key Factors Influencing Autonomous Vehicles’ Energy and Environmental Outcome

Autonomous vehicles (AVs)—vehicles that operate without real-time human input—are a potentially disruptive technology. If widely adopted, there is the potential for significant impacts on the energy and environmental characteristics of the transportation sector. This paper provides an outline of key drivers likely to influence the magnitude and direction of these impacts. We identify three broad categories: vehicle characteristics, transportation network, and consumer choice. Optimistically, AVs could facilitate unprecedented levels of efficiency and radically reduce transportation sector energy and environmental impacts; on the other hand, consumer choices could result in a net increase in energy consumption and environmental impacts. As the technology matures and approaches market penetration, improved models of AV usage, especially consumer preferences, will facilitate the development of policies that promote reductions in energy consumption.

A Wedge-Based Approach to Estimating Health Co-Benefits of Climate Change Mitigation Activities in the United States

While it has been recognized that actions reducing greenhouse gas (GHG) emissions can have significant positive and negative impacts on human health through reductions in ambient fine particulate matter (PM 2.5 ) concentrations, these impacts are rarely taken into account when analyzing specific policies. This study presents a new framework for estimating the change in health outcomes resulting from implementation of specific carbon dioxide (CO 2 ) reduction activities, allowing comparison of different sectors and options for climate mitigation activities. Our estimates suggest that in the year 2020, the reductions in adverse health outcomes from lessened exposure to PM 2.5 would yield economic benefits in the range of

Modeling the climate impacts of deploying solar reflective cool pavements in California cities

6 to

Reflective ‘cool’ roofs under aerosol-burdened skies: radiative benefits across selected Indian cities

30 billion (in 2008 USD), depending on the specific activity. This equates to between

Tracking the Sun: Installed Price Trends for Distributed Photovoltaic Systems in the United States - 2018 Edition

40 and

Datasets on hub-height wind speed comparisons for wind farms in California

198 per metric ton of CO 2 in health benefits. Specific climate interventions will vary in the health co-benefits they provide as well as in potential harms that may result from their implementation. Rigorous assessment of these health impacts is essential for guiding policy decisions as efforts to reduce GHG emissions increase in scope and intensity. Copyright Springer Science+Business Media Dordrecht (outside the USA) 2014

Wind energy variability and links to regional and synoptic scale weather

Solar reflective “cool pavements” have been proposed as a potential heat mitigation strategy for cities. However, previous research has not systematically investigated the extent to which cool pavements could reduce urban temperatures. In this study we investigated the climate impacts of widespread deployment of cool pavements in California cities. Using the weather research and forecasting model (WRF), we simulated the current climate of California at 4 km spatial resolution. Comparing this simulation to 105 weather stations in California suggested an overall mean bias (model minus observation) of -0.30 °C. Widespread pavement albedo increases of 0.1 and 0.4 in California cities were then simulated. Comparing temperature reductions for each scenario showed that the climate response to pavement albedo modification was nearly linear. Temperature reductions at 14:00 local standard time were found to be 0.32 °C per 0.1 increase in grid cell average albedo. Temperature reductions were found to peak in the late morning and evening when boundary layer heights were low, and solar irradiance (late morning) or heat accumulation in the pavement (evening) was high. Summertime temperature reductions were found to be larger than corresponding reductions during winter, as expected. After scaling the results using realistic data-derived urban canyon morphologies and an offline urban canyon albedo model, annual average surface air temperature reductions from increasing pavement albedo by 0.4 ranged from 0.18 °C (Palm Springs) to 0.86 °C (San Jose). The variation among cities was due to differences in baseline climate, size of the city, urban fraction, and urban morphology.