Robert Margolis

Harnessing the sun

Now is the time to plan for the integration of significant quantities of solar energy into the electricity grid. Although solar energy constitutes a very small portion of our energy system today, the size of the resource is enormous: The earth receives more energy from the sun in one hour than the global population uses in an entire year. In addition, the solar photovoltaic (PV) industry is growing very rapidly, sustaining an annual growth rate of more than 40% for the last decade. The combination of this rapid growth, falling costs, and a vast technical potential could make solar energy a serious contender for meeting our future energy needs.

Supply-chain dynamics of tellurium, indium and gallium within the context of PV module manufacturing costs

If humankind is to implement more sustainable energy choices, it will be crucial for energy systems such as photovoltaics (PV) to demonstrate success both soon and over the long-term quest. To that end, both the crystalline silicon and thin-film technologies have made, and continue to make, remarkable strides toward providing solutions that are quickly becoming more competitive against the traditional sources for power generation. But, within the thin-film segment of this industry the highest demonstrated sunlight power conversion efficiencies have thus far come from material sets containing relatively rare constituent elements. These include tellurium in the cadmium telluride technology, and indium and/ or gallium in the CIS/copper indium gallium diselenide and III-V families of technologies. In this paper we show that the current global supply base for these three energy-critical elements is not sufficient for enabling energy-significant levels of PV deployment, but also show that each of the thin-film PV technologies that are described has an ability to absorb potential increases in the price for these constituent element(s). This ability then leads to the possibility that the supply base for each element can be augmented.Given the need for humankind to implement more sustainable energy choices, it is crucial for energy systems such as PV to demonstrate success both soon and over the long-term quest for meaningful deployment. To that end, both the crystalline silicon and thin-film technologies have made, and continue to make, remarkable strides toward providing solutions that are quickly becoming more competitive against the traditional sources for power generation. But, within the thin-film segment of this industry, the highest demonstrated sunlight power conversion efficiencies have thus far come from technologies containing relatively rare constituent elements. These include tellurium in cadmium telluride, and indium and/or gallium in the CIS/ CIGS and III–V families of technologies. In this paper we show that the current global supply base for these three energy-critical elements is not sufficient for enabling energy-significant levels of deployment, but also show that every one of the thin-film PV technologies that we describe has the ability to absorb an increase in the price for each constituent element(s). This ability then leads to the possibility that the supply base for each element can be augmented.

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.

Letting the Sun Shine on Solar Costs: An Empirical Investigation of Photovoltaic Cost Trends in California

This report provides a comprehensive analysis of grid-connected solar photovoltaic (PV) cost trends in California, which is by far the largest PV market in the United States. The findings of this work may help stakeholders to understand important trends in the California PV market, and policymakers to design more effective solar incentive programs--a particularly important objective given the recent announcement from the California Public Utilities Commission (CPUC) to establish an 11-year,

Exploring the market for third-party-owned residential photovoltaic systems: insights from lease and power-purchase agreement contract structures and costs in California

3.2 billion incentive program for customer-sited solar. The study statistically analyzes the installed cost of grid-connected PV systems funded by the states two largest solar rebate programs, overseen by the California Energy Commission (CEC) [operating since 1998] and the CPUC [operating since 2001].

Photovoltaic Investment Risk and Uncertainty for Residential Customers

Over the past several years, third-party-ownership (TPO) structures for residential photovoltaic (PV) systems have become the predominant ownership model in the US residential market. Under a TPO contract, the PV system host typically makes payments to the third-party owner of the system. Anecdotal evidence suggests that the total TPO contract payments made by the customer can differ significantly from payments in which the system host directly purchases the system. Furthermore, payments can vary depending on TPO contract structure. To date, a paucity of data on TPO contracts has precluded studies evaluating trends in TPO contract cost. This study relies on a sample of 1113 contracts for residential PV systems installed in 2010–2012 under the California Solar Initiative to evaluate how the timing of payments under a TPO contract impacts the ultimate cost of the system to the customer. Furthermore, we evaluate how the total cost of TPO systems to customers has changed through time, and the degree to which contract costs have tracked trends in the installed costs of a PV system. We find that the structure of the contract and the timing of the payments have financial implications for the customer: (1) power-purchase contracts, on average, cost more than leases, (2) no-money-down contracts are more costly than prepaid contracts, assuming a customer’s discount rate is lower than 17% and (3) contracts that include escalator clauses cost more, for both power-purchase agreements and leases, at most plausible discount rates. In addition, all contract costs exhibit a wide range, and do not parallel trends in installed costs over time.

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

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.

Modeling Photovoltaic Diffusion: An Analysis of Geospatial Datasets

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.

An economic analysis of photovoltaics versus traditional energy sources: Where are we now and where might we be in the near future?

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.