Easan Drury

Solar Deployment System (SolarDS) Model: Documentation and Sample Results

The Solar Deployment System (SolarDS) model is a bottom-up, market penetration model that simulates the potential adoption of photovoltaics (PV) on residential and commercial rooftops in the continental United States through 2030. NREL developed SolarDS to examine the market competitiveness of PV based on regional solar resources, capital costs, electricity prices, utility rate structures, and federal and local incentives. The model uses the projected financial performance of PV systems to simulate PV adoption for building types and regions then aggregates adoption to state and national levels. The main components of SolarDS include a PV performance simulator, a PV annual revenue calculator, a PV financial performance calculator, a PV market share calculator, and a regional aggregator. The model simulates a variety of installed PV capacity for a range of user-specified input parameters. PV market penetration levels from 15 to 193 GW by 2030 were simulated in preliminary model runs. SolarDS results are primarily driven by three model assumptions: (1) future PV cost reductions, (2) the maximum PV market share assumed for systems with given financial performance, and (3) PV financing parameters and policy-driven assumptions, such as the possible future cost of carbon emissions.

Bright Future: Solar Power as a Major Contributor to the U.S. Grid

The decreased costs of solar technologies have led to the prospect of a move for photovoltaic (PV ) and concentrating solar power (CSP ) from niche applications to major contributors to the U.S. electricity grid. This development has motivated a number of technoeconomic analyses of the potential deployment of both PV and CSP under varying economic conditions. Two studies sponsored by the U.S. Department of Energy (DOE ) and completed in 2012 can help us understand the potential opportunities and challenges for solar deployment on a large scale. These studies evaluated both the potential mix of renewable energy technologies that could serve a large fraction of the U.S. electricity demand and the associated evolution of the U.S. grid to 2050.

Photovoltaic Investment Risk and Uncertainty for Residential Customers

The revenues generated by rooftop photovoltaic (PV) systems have several sources of uncertainty. We use a Monte Carlo framework to explore the sensitivity of PV investment returns to three categories of PV investment uncertainty: 1) interannual solar variability, 2) PV technical performance and maintenance costs, and 3) market risks including future electricity rates and the possibility that retail electricity rates will be restructured for PV customers. We find that PV investment risk and uncertainty is driven by market factors in some U.S. regions (California and Massachusetts) and by the PV technical performance in other U.S. regions (Missouri and Florida). We explore the relative impacts of three methods for reducing PV investment uncertainty: research-and-development-driven performance improvements, system performance guarantees that are common for third-party owned systems, and long-term power purchase contracts. We find that the effectiveness of each risk reduction option varies by region, depending on which factors drive regional PV investment uncertainty.

The solar photovoltaics wedge: pathways for growth and potential carbon mitigation in the US

The challenge of stabilizing global carbon emissions over the next 50 years has been framed in the context of finding seven 1.0 Gton C/year carbon reduction wedges. Solar photovoltaics (PV) could provide at least one carbon wedge, but will require significant growth in PV manufacturing capacity. The actual amount of installed PV capacity required to reach wedge-level carbon reductions will vary greatly depending on the mix of avoided fuels and the additional emissions from manufacturing PV capacity. In this work, we find that the US could reduce its carbon emissions by 0.25 Gton C/year, equal to the fraction of a global carbon wedge proportional to its current domestic electricity use, by installing 792‐811 GW of PV capacity. We evaluate a series of PV growth scenarios and find that wedge-level reductions could be met by increasing PV manufacturing capacity and annual installations by 0.95 GW/year/year each year from 2009 to 2050 or by increasing up to 4 GW/year/year for a period of 4‐17 years for early and late growth scenarios. This challenge of increasing PV manufacturing capacity and market demand is significant but not out of line with the recent rapid growth in both the global and US PV industry. We find that the rapid growth in PV manufacturing capacity leads to a short term increase in carbon emissions from the US electric sector. However, this increase is small, contributing less than an additional 0.3% to electric sector emissions for less than 4.5 years, alleviating recent concern regarding carbon emissions from rapid PV growth scenarios.

Modeling Photovoltaic Diffusion: An Analysis of Geospatial Datasets

This study combines address-level residential photovoltaic (PV) adoption trends in California with several types of geospatial information—population demographics, housing characteristics, foreclosure rates, solar irradiance, vehicle ownership preferences, and others—to identify which subsets of geospatial information are the best predictors of historical PV adoption. Number of rooms, heating source and house age were key variables that had not been previously explored in the literature, but are consistent with the expected profile of a PV adopter. The strong relationship provided by foreclosure indicators and mortgage status have less of an intuitive connection to PV adoption, but may be highly correlated with characteristics inherent in PV adopters. Next, we explore how these predictive factors and model performance varies between different Investor Owned Utility (IOU) regions in California, and at different spatial scales. Results suggest that models trained with small subsets of geospatial information (five to eight variables) may provide similar explanatory power as models using hundreds of geospatial variables. Further, the predictive performance of models generally decreases at higher resolution, i.e., below ZIP code level since several geospatial variables with coarse native resolution become less useful for representing high resolution variations in PV adoption trends. However, for California we find that model performance improves if parameters are trained at the regional IOU level rather than the state-wide level. We also find that models trained within one IOU region are generally representative for other IOU regions in CA, suggesting that a model trained with data from one state may be applicable in another state.

Solar Energy: The Largest Energy Resource

This chapter discusses the solar energy as the largest energy resource. The fraction of electricity generated by solar technologies is small but growing rapidly, with enormous potential to generate a large fraction of the worlds electricity needs while significantly reducing global carbon emissions. Realizing this potential, however, will require overcoming both technical and economic barriers. In the short term, it will be important to decrease costs, improve solar conversion efficiency, and implement electricity rate structures that capture the time-varying value of solar-generated electricity. In the long term, challenges will include using material resources more efficiently, integrating intermittent photovoltaic electricity into the grid, and building transmission capacity for utility-scale solar generation systems linking areas with good solar resources to population centers.

Potential Role of Concentrating Solar Power in Enabling High Renewables Scenarios in the United States

This work describes the analysis of concentrating solar power (CSP) in two studies -- The SunShot Vision Study and the Renewable Electricity Futures Study -- and the potential role of CSP in a future energy mix.

Sensitivity of Rooftop PV Projections in the SunShot Vision Study to Market Assumptions

The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The SolarDS model was used to simulate rooftop PV demand for this study, based on several PV market assumptions--future electricity rates, customer access to financing, and others--in addition to the SunShot PV price projections. This paper finds that modeled PV demand is highly sensitive to several non-price market assumptions, particularly PV financing parameters.

Exploring large-scale solar deployment in DOE's SunShot Vision Study

Large-scale solar deployment is sensitive to several factors including future solar technology price and performance, electricity demand, and the challenges and benefits of integrating solar generation resources. The SunShot Initiative was launched by the U.S. Department of Energy (DOE) in 2011 aimed at reducing the cost of solar electricity by about 75% from 2010 to 2020, whereby solar energy could compete with conventional electricity sources without subsidies. DOE conducted the SunShot Vision Study to evaluate the potential impacts of achieving these price and performance improvements, and the underlying modeling analysis suggests that solar energy could satisfy roughly 14% of U.S. electricity demand by 2030 and 27% by 2050. This growth of solar electricity could decrease electric sector carbon emissions by 28% and decrease retail electricity rates by 7% in the year 2050, relative to a reference scenario.

Chapter 13: Prospects for Renewable Energy

Publisher Summary This chapter examines availability, markets, and the technical potential of renewable energy (RE) resources in meeting energy demand in a redefined energy economy. These new energy challenges include energy security, environmental integrity, climate change, and economic prosperity. It looks at how far renewables have come during the past decades and their potential to provide a larger portion of energy needs in the future. It also examines the current status of renewables capacity as well as technology investment; it includes trends by country and by technology as well as the impact of the global economic situation. Market and technology trends for resources such as hydropower, wind, solar, geothermal, and bioenergy; it also provides market projections are also outlined.