Unlocking the Full Potential of Rooftop PV In India Using Consumer Adoption Modeling [Slides]
This slide deck provides an analysis about adoption of Distributed PV in India using NREL's dGen model.
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Engineering topics
Ramdas, Ashwin
Publications and source records attributed to Ramdas, Ashwin.
Benchmarks of Global Clean Energy Manufacturing, 2014-2016
The Clean Energy Manufacturing Analysis Center (CEMAC), sponsored by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE), provides objective analysis and up-to-date data on global supply chains and manufacturing of clean energy technologies. This updated version of the Benchmarks of Global Clean Energy Manufacturing sheds light on several fundamental questions about the global clean technology manufacturing enterprise: How does clean energy technology manufacturing impact national economies? What are the economic opportunities across the manufacturing supply chain? What are the global dynamics of clean energy technology manufacturing?
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Revisiting the Effects of Photovoltaic Module Efficiency on Installed System Cost and Market Adoption
We conduct bottom-up analysis of how PV module efficiency could influence residential rooftop installed system costs, using models with improved representation of how soft costs and power electronics are affected by efficiency. We show that the amount of system installed cost savings resulting from high efficiency modules depends on whether or not the system is area-constrained, with much larger savings for the area-constrained case. We then estimate the fraction of physically area-constrained rooftops in the United States to be 61%, with variation between 20% to over 80% of rooftops depending on the state. We find that fraction of area-constrained systems could grow to over 88% by 2050 if building and transportation loads are electrified.
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U.S. Solar Photovoltaic System and Energy Storage Cost Benchmark (Q1 2020)
This report benchmarks U.S. solar photovoltaic (PV) system installed costs as of the first quarter of 2020 (Q1 2020). We use a bottom-up method, accounting for all system and project-development costs incurred during the installation to model the costs for residential (with and without storage), commercial (with and without storage), and utility-scale systems (with and without storage). We also incorporate other system characteristics and performance factors, as well as ongoing costs, to benchmark LCOE for PV-standalone systems, and LCOSS for solar+storage.
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U.S. Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2020 [PowerPoint]
NREL has been modeling U.S. photovoltaic (PV) system costs since 2009. This report benchmarks costs of U.S. solar PV for residential, commercial, and utility-scale systems, with and without storage, built in the first quarter of 2020 (Q1 2020). Our methodology includes bottom-up accounting for all system and project-development costs incurred when installing residential, commercial, and utility-scale systems, and it models the capital costs for such systems.
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The Effect of Photovoltaic Module Efficiency on Installed System Costs and Markets in Residential Rooftop Installations in the United States
In this report, we analyze how efficiencies influence residential PV system installed system costs and break-even module prices using a bottom-up cost model. We show that the cost savings associated with efficiency are much higher for area-constrained rooftop than those which are usage-constrained. We also show that simplified models of how efficiency impacts costs overestimate the value of efficiency compared to bottom-up models, particularly for usage-constrained scenarios. We also estimate the fraction of area-constrained residential rooftops in the United States today and explore how that fraction could change in future scenarios with high levels of electrification.
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Distributed Generation Market Demand (dGen) model
The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs.
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Annual Technology Baseline: The 2020 Electricity Update
Consistent cost and performance data for various electricity generation technologies can be difficult to find and may change frequently for certain technologies. With the Annual Technology Baseline (ATB), the National Renewable Energy Laboratory annually provides an organized and centralized set of such cost and performance data. The ATB uses the best information from the Department of Energy national laboratories' renewable energy analysts. The ATB has been reviewed by experts and it includes the following electricity generation technologies: land-based wind, offshore wind, utility-scale solar photovoltaics (PV), commercial-scale solar PV, residential-scale solar PV, concentrating solar power, geothermal power, hydropower, utility-scale battery storage. EIA data for coal, natural gas, nuclear, and conventional biopower are included for reference. This webinar presentation introduces the 2020 update to the ATB Electricity data and documentation.
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