• Energy Research

In support of the national goals for biofuel use in the United States, numerous technologies have been developed that convert biomass to biofuels. Some of these biomass to biofuel conversion technology pathways are operating at commercial scales, while others are in earlier stages of development. The advancement of a new pathway toward commercialization involves various types of progress, including yield improvements, process engineering, and financial performance. Actions of private investors and public programs can accelerate the demonstration and deployment of new conversion technology pathways. These investors (both private and public) will pursue a range of pilot, demonstration, and pioneer scale biorefinery investments; the most cost-effective set of investments for advancing the maturity of any given biomass to biofuel conversion technology pathway is unknown. In some cases, whether or not the pathway itself will ultimately be technically and financially successful is also unknown. This report presents results from the Biomass Scenario Model -- a system dynamics model of the biomass to biofuels system -- that estimate effects of investments in biorefineries at different maturity levels and operational scales. The report discusses challenges in estimating effects of such investments and explores the interaction between this deployment investment and a volumetric production incentive.more » Model results show that investments in demonstration and deployment have a substantial positive effect on the development of the biofuels industry. Results also show that other conditions, such as supportive policies, have major impacts on the effectiveness of such investments.« less

The year 2014 marked hydrogen fuel cell electric vehicles (FCEVs) first becoming commercially available in California, where significant investments are being made to promote the adoption of alternative transportation fuels. A refueling infrastructure network that guarantees adequate coverage and expands in line with vehicle sales is required for FCEVs to be successfully adopted by private customers. In this paper, we provide an overview of modelling methodologies used to project hydrogen refueling infrastructure requirements to support FCEV adoption, and we describe, in detail, the National Renewable Energy Laboratory’s scenario evaluation and regionalization analysis (SERA) model. As an example, we use SERA to explore two alternative scenarios of FCEV adoption: one in which FCEV deployment is limited to California and several major cities in the United States; and one in which FCEVs reach widespread adoption, becoming a major option as passenger vehicles across the entire country. Such scenarios can provide guidance and insights for efforts required to deploy the infrastructure supporting transition toward different levels of hydrogen use as a transportation fuel for passenger vehicles in the United States.

This study provides a framework to explore the potential use and incremental value of small- to large-scale penetration of solar and wind technologies as a physical hedge against the risk and uncertainty of electricity cost on multi-year to multi-decade timescales. Earlier studies characterizing the impacts of adding renewable energy (RE) to portfolios of electricity generators often used a levelized cost of energy or simplified net cash flow approach. In this study, we expand on previous work by demonstrating the use of an 8760 hourly production cost model (PLEXOS) to analyze the incremental impact of solar and wind penetration under a wide range of penetration scenarios for a region in the Western U.S. We do not attempt to 'optimize' the portfolio in any of these cases. Rather we consider different RE penetration scenarios, that might for example result from the implementation of a Renewable Portfolio Standard (RPS) to explore the dynamics, risk mitigation characteristics and incremental value that RE might add to the system. We also compare the use of RE to alternative mechanisms, such as the use of financial or physical supply contracts to mitigate risk and uncertainty, including consideration of their effectiveness and availability over a variety of timeframes.

Abstract To assess the electric power grid environment under the high penetration of photovoltaic (PV) generation, it is important to construct an accurate representation of PV power output for any location in the southwestern United States at resolutions down to 10-min time steps. Existing analyses, however, typically depend on sparsely spaced measurements and often include modeled data as a basis for extrapolation. Consequentially, analysts have been confronted with inaccurate analytic outcomes due to both the quality of the modeled data and the approximations introduced when combining data with differing space/time attributes and resolutions. This study proposes an accurate methodology for 10-min PV estimation based on the self-consistent combination of data with disparate spatial and temporal characteristics. Our Type I estimation uses the nearby locations of temporally detailed PV measurements, whereas our Type II estimation goes beyond the spatial range of the measured PV incorporating alternative data set(s) for areas with no PV measurements; those alternative data sets consist of: (1) modeled PV output and secondary cloud cover information around space/time estimation points, and (2) their associated uncertainty. The Type I estimation identifies a spatial range from existing PV sites (30–40 km), which is used to estimate accurately 10-min PV output performance. Beyond that spatial range, the data-quality-control estimation (Type II) demonstrates increasing improvement over the Type I estimation that does not assimilate the uncertainty of data sources. The methodology developed herein can assist the evaluation of the impact of PV generation on the electric power grid, quantify the value of measured data, and optimize the placement of new measurement sites.

The surface land use of fossil fuel acquisition and utilization has not been well characterized, inhibiting consistent comparisons of different electricity generation technologies. Here we present a method for robust estimation of the life cycle land use of electricity generated from natural gas through a case study that includes inventories of infrastructure, satellite imagery and well-level production. Approximately 500 sites in the Barnett Shale of Texas were sampled across five life cycle stages (production, gathering, processing, transmission and power generation). Total land use (0.62 m2 MWh−1, 95% confidence intervals ±0.01 m2 MWh−1) was dominated by midstream infrastructure, particularly pipelines (74%). Our results were sensitive to power plant heat rate (85–190% of the base case), facility lifetime (89–169%), number of wells per site (16–100%), well lifetime (92–154%) and pipeline right of way (58–142%). When replicated for other gas-producing regions and different fuels, our approach offers a route to enable empirically grounded comparisons of the land footprint of energy choices. Understanding land-use requirements for the life cycle of electricity generation allows for consistent comparisons of different technologies. Jordaan et al. present a method that leverages highly resolved, empirical data sets to give robust estimates of land use for natural-gas-fired electricity.

AbstractThe Biomass Scenario Model (BSM) is a system‐dynamics simulation model intended to explore the potential for rapid expansion of the biofuels industry. The model is not predictive — it uses scenario assumptions based on various types of data to simulate industry development, emphasizing how incentives and technological learning‐by‐doing might accelerate industry growth. The BSM simulates major sectors of the biofuels industry, including feedstock production and logistics, conversion, distribution, and end uses, as well as interactions among sectors. The model represents conversion of biomass to biofuels as a set of technology pathways, each of which has allowable feedstocks, capital and operating costs, allowable products, and other defined characteristics. This study and the BSM address bioenergy modeling analytic needs that were identified in recent literature reviews. Simulations indicate that investments are most effective at expanding biofuels production through learning‐by‐doing when they are coordinated with respect to timing, pathway, and target sector within the biofuels industry. Effectiveness metrics include timing and magnitude of increased production, incentive cost and cost effectiveness, and avoidance of windfall profits. Investment costs and optimal investment targets have inherent risks and uncertainties, such as the relative value of investment in more‐mature versus less mature pathways. These can be explored through scenarios, but cannot be precisely predicted. Dynamic competition, including competition for cellulosic feedstocks and ethanol market shares, intensifies during times of rapid growth. Ethanol production increases rapidly, even up to Renewable Fuel Standards‐targeted volumes of biofuel, in simulations that allow higher blending proportions of ethanol in gasoline‐fueled vehicles. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. Biofuels, Bioproducts, Biorefining published by John Wiley & Sons, Ltd on behalf of Society of Chemical Industry.

Increasing demand for crop-based biofuels, in addition to other human drivers of land use, induces direct and indirect land use changes (LUC). Our system dynamics tool is intended to complement existing LUC modeling approaches and to improve the understanding of global LUC drivers and dynamics by allowing examination of global LUC under diverse scenarios and varying model assumptions. We report on a small subset of such analyses. This model provides insights into the drivers and dynamic interactions of LUC (e.g., dietary choices and biofuel policy) and is not intended to assert improvement in numerical results relative to other works. Demand for food commodities are mostly met in high food and high crop-based biofuel demand scenarios, but cropland must expand substantially. Meeting roughly 25% of global transportation fuel demand by 2050 with biofuels requires >2 times the land used to meet food demands under a presumed 40% increase in per capita food demand. In comparison, the high food demand scenario requires greater pastureland for meat production, leading to larger overall expansion into forest and grassland. Our results indicate that, in all scenarios, there is a potential for supply shortfalls, and associated upward pressure on prices, of food commodities requiring higher land use intensity (e.g., beef) which biofuels could exacerbate.

The Energy Independence and Security Act of 2007 targets use of 36 billion gallons of biofuels per year by 2022. Achieving this may require substantial changes to current transportation fuel systems for distribution, dispensing, and use in vehicles. The U.S. Department of Energy and the National Renewable Energy Laboratory designed a system dynamics approach to help focus government action by determining what supply chain changes would have the greatest potential to accelerate biofuels deployment. The National Renewable Energy Laboratory developed the Biomass Scenario Model, a system dynamics model which represents the primary system effects and dependencies in the biomass-to-biofuels supply chain. The model provides a framework for developing scenarios and conducting biofuels policy analysis. This paper focuses on the downstream portion of the supply chain-represented in the distribution logistics, dispensing station, and fuel utilization, and vehicle modules of the Biomass Scenario Model. This model initially focused on ethanol, but has since been expanded to include other biofuels. Some portions of this system are represented dynamically with major interactions and feedbacks, especially those related to a dispensing station owner's decision whether to offer ethanol fuel and a consumer's choice whether to purchase that fuel. Other portions of the system are modeled with little or no dynamics; the vehicle choices of consumers are represented as discrete scenarios. This paper explores conditions needed to sustain an ethanol fuel market and identifies implications of these findings for program and policy goals. A large, economically sustainable ethanol fuel market (or other biofuel market) requires low end-user fuel price relative to gasoline and sufficient producer payment, which are difficult to achieve simultaneously. Other requirements (different for ethanol vs. other biofuel markets) include the need for infrastructure for distribution and dispensing and widespread use of high ethanol blends in flexible-fuel vehicles.