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DOE OSTI2015

Capacity Adequacy and Revenue Sufficiency in Electricity Markets With Wind Power

We present a computationally efficient mixed-integer program (MIP) that determines optimal generator expansion decisions, as well as periodic unit commitment and dispatch. The model is applied to analyze the impact of increasing wind power capacity on the optimal generation mix and the profitability of thermal generators. In a case study, we find that increasing wind penetration reduces energy prices while the prices for operating reserves increase. Moreover, scarcity pricing for operating reserves through reserve shortfall penalties significantly impacts the prices and profitability of thermal generators. Without scarcity pricing, no thermal units are profitable, however scarcity pricing can ensure profitability for peaking units at high wind penetration levels. Capacity payments can also ensure profitability, but the payments required for baseload units to break even increase with the amount of wind power. The results indicate that baseload units are most likely to experience revenue sufficiency problems when wind penetration increases and new baseload units are only developed when natural gas prices are high and wind penetration is low.

capacity adequacy↗

DOE OSTI2019

Evaluation of Hydrogen Production Feasibility for a Light Water Reactor in the Midwest

Increased electricity production from renewable energy resources coupled with low natural gas prices has caused existing light-water reactor (LWRs) to experience ever diminishing returns from the electricity market. Via partnership among Idaho National Laboratory (INL), The National Renewable Energy Laboratory (NREL), Argonne National Laboratory (ANL), Exelon, and Fuel Cell Energy a techno-economic analysis on the viability of retrofitting existing pressurized water reactors (PWRs) to produce hydrogen (H2) via high temperature steam electrolysis (HTSE) has been conducted. Such integration would allow nuclear facilities to expand into additional markets that may be more profitable in the long-term. To accommodate such an integration, a detailed analysis of HTSE process operation, requirements, and flexibility was conducted. Technical analysis includes proposed nuclear system control scheme modifications to allow dynamic operation of the HTSE via both thermal and electrical connection to the nuclear plant. High fidelity Modelica simulations showcase the viability of such control schemes. However, due to limited knowledge of solid oxide fuel cell (SOFC) stack degradation due to thermal gradients; thermal cycling of the HTSE was not included. Therefore, the control schemes proposed are only utilized to re-distribute steam at startup and only the portion of electricity utilized in the electrolyzers is cycled. From the detailed analysis of the nuclear integration and the HTSE process design, a comprehensive cost estimation was conducted in the APEA and H2A models to elucidate capital and operational costs associated with the production, compression, and distribution of hydrogen from a nuclear facility. Alongside this costing analysis, market analyses were conducted by NREL and ANL on the electric and hydrogen markets respectively in the PJM interconnect. Utilizing the electricity data market projections in the PJM interconnect from NREL and hydrogen demand/pricing projections from ANL a five variable sweep over component capacities, discount rates, and hydrogen pricing was completed using the stochastic framework RAVEN (Risk Analysis Virtual ENVironment) through its resource dispatch plugin HERON (Heuristic Energy Resource Optimization Network). Each combination of variables was evaluated stochastically four times over a seventeen year timespan from 2026-2042 (inclusive) to determine the most economically advantageous solution. Following the five variable sweep an optimization was conducted about the best sweep point to determine optimal component sizing and setpoints. Results suggest positive gain is achievable at all projected hydrogen market pricing levels and at all discount rates. However, exact component sizing and net returns vary based on these values and if incorrect sizing is selected; major net losses can occur. The optimal result occurred with set points as follows: high hydrogen prices, the largest possible HTSE unit in the sweep set at 7.47 kg/sec [645.4 tpd], a contractual hydrogen market agreement 7.29 kg/sec [629.8 tpd], and a hydrogen storage size 115,188 kg. The analysis suggested that with a discount rate of 8%, a ΔNPV = 1.2 billion over the seventeen year span can be achieved. The results illuminate that by operating in multiple markets the nuclear facility is capable of avoiding sale of electricity during times of low electricity market pricing, while maintaining the ability to capitalize on the high electricity market pricing. It should be noted that the analysis conducted in this report is a differential cash flow analysis and as such does not present profit levels. Instead it highlights the net benefit between building and competing in the hydrogen market utilizing nuclear facilities and conducting business as usual in the electricity markets. Additionally, results presented in this report exhibit conservatism due to four key assumptions: Given the limited knowledge on SOFC stack degradation due to thermal gradients the high temperature steam electrolysis plant is not allowed to thermally cycle. This limitation decreases electricity generation capacity in the nuclear plant, reducing capacity payments and the maximum output to the electrical grid. This analysis considers building a separate hydrogen pipeline for use in the nuclear facility. Research is currently being conducted to determine if existing natural gas pipelines can accommodate direct hydrogen injection. If hydrogen can be integrated into existing natural gas pipelines then a capital savings of ~$19,000,000 per kg/sec [1 kg/sec = 86.4 tpd] of installed HTSE capacity could be realized. Fast transients in the electric grid are typically served by flywheels and electric batteries. With electrolysis only operation the nuclear plant can flex its electricity just as quickly. However, for this analysis, the ancillary services market has been neglected. Should the nuclear plant have the ability to operate in the ancillary services market during periods when it is producing hydrogen, an additional substantial income stream that would increase overall system profitability could be considered. Subsidies for non-emitting generation technologies are not introduced in the cash flow analysis, but instead are completed assuming zero subsidies. Profitability is based solely on electricity pricing. Electric pricing is assumed to never go negative. Instead, it is assumed overall system curtailment by the Independent System Operator (ISO) will occur in such a manner that pricing remains at or above zero. This assumption inflates the “business as usual” scenario, thus decreasing the ΔNPV calculated. If negative pricing scenarios continue to occur as they do today, one would expect a further increase in ΔNPV. Overall, results advocate that, through market diversification, nuclear power plants have the potential to substantially increase current profit margins, increase market penetration, and ultimately solidify their place as a mainstay in energy production.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Stochastic Analysis for Long Term Capital Structures, Systems, and Components Refurbishment and Replacement

As commercial Nuclear Power Plants (NPPs) pursue extended plant operation in the form of Second License Renewal (SLR), opportunities exist for these plants to provide capital investments to ensure long-term safe and economic performance. At the current time, several utilities have announced an intention to pursue extended operation for one or more of their NPPs via SLR . The goal of this research is to develop a risk-informed approach to evaluate and prioritize plant capital investments made in preparation for, and during the period of, extended plant operations to support decisions for NPP operations. Since the capital investments are influenced by various factors, such as markets, safety and regulatory, the decision-making process of NPP operations should take into account relevant factors for balancing risks, costs and profits. The traditional method of capital budgeting is based on the priority list of candidate projects using economic measures such as benefit-investment ratio, net present value (NPV) and internal rate of return. In the literatures, the problem of capital budgeting or the variant can be represented by an appropriate knapsack problem. The knapsack approach to capital budgeting takes as input as investment, along with the cost and profit of each project. The objective of capital budgeting is to find the combination of the binary decisions for every investment such that the overall profit is as large as possible. The output is a collection of projects to be carried out, and we refer this selected collection of projects as a project portfolio. One limitation of traditional optimization models for capital budgeting is that they do not account for risk/uncertainty in profit and cost streams associated with individual projects, they do not account for risk in resource availability in future years [1,2,3]. Projects can incur cost over-runs, especially when projects are large, performed infrequently, and when there is risk regarding technical viability, external contractors, and/or suppliers of requisite parts and materials. Occasionally, projects are performed ahead of schedule and with cost savings. Planned budgets for capital improvements can be cut and key personnel may be lost. Or, there may be surprise windfalls in budgets for maintenance activities due to decreased costs for “unplanned” maintenance. In these cases, how should we resolve capital budgeting when we have risk forecasts for costs, profits and budgets? One approach we proposed in this summary is to re-solve the optimization models based on assumed statistical distributions of given parameters. If these distributions were not available, a two-stage stochastic optimization approach can be used to provide priority lists to decision-makers to support better risk-informed decisions [4, 5]. In this summary, we will only focus on the first approach.

42 ENGINEERING↗

DOE OSTI2017

An Integrated Landscape Management Approach to Sustainable Bioenergy Production

Here, integrated landscape management has emerged in recent years as a methodology to integrate the environmental impacts of various agricultural practices along with yield and profitability in a variety of cropping systems. In this study, the Landscape Environmental Assessment Framework (LEAF), a decision support toolset for use in integrated landscape management and developed at Idaho National Laboratory, is used to evaluate the profitability of grain producing subfields and determining the efficacy of harvesting residual biomass after grain harvest while ensuring that the soil and soil carbon losses from wind and water erosion remain low enough to sustainably continue year-to-year production of grains. LEAF was used to assess the potential production of three bioenergy crops, sorghum, switchgrass, and miscanthus, on the unprofitable subfields in four US counties. To assess the sustainability of this mode of bioenergy production, the harvesting of residual biomass and energy crops are modeled using geo-referenced, precision harvesting equipment and optimal harvesting paths on individual sub-fields to develop farm-specific estimates of harvesting costs. The resulting average harvesting and logistics costs for combined harvesting of residual biomass and energy crops were observed to be well within US DOE’s 2017 goals for biomass feedstock price. However, it was also determined that considerable variability exists in the harvesting costs because of the size, shape, and productivity of individual subfields. It was determined that the variability in the harvesting costs can be used as a means to cull non-profitable farms that also have low harvesting costs and high yields of bioenergy crops. The results of the assessment indicate that the potential to produce biomass is considerably enhanced when non-profitable subfields are replaced by bioenergy crops to be harvested as biomass.

09 BIOMASS FUELS↗

DOE OSTI2018

Stochastic risk-sensitive market integration for renewable energy: Application to ocean wave power plants

With the expected increase of intermittent renewable energy resources on the electric power grid, short-term reserve markets can prove to be a critical reliability asset. This paper introduces and tests a potential market structure that includes a short-term reserve market for renewable resources where the realized energy is drawn within the bounds of the hourly capacity offered. The study presents a risk-aware stochastic model that determines, based on the day-ahead offer derived using a classic newsvendor formula, the best intraday offers for a renewable energy power plant. The stochastic approach takes into account the uncertainties of energy production and market prices. The proposed risk-sensitive model aims to maximize the renewable power plant's revenues and minimize potential risks of loss in a multi-product multi-timescale market setup. To demonstrate the effectiveness of the proposed formulation, we study the case of a hypothetical 750-kW wave power plant coupled with energy storage. The study shows that the risk of profit loss is not uniformly distributed across the risk-aversion factor space. We also find that the introduction of the short-term reserve market results in a wider range of conditional value at risk while inducing 5% profit increase and a lower profit reduction across the risk range. In addition, the study reveals that the short-term reserve market is profitable for both the system operator and the wave energy power plant considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

A robust offering strategy for wind producers considering uncertainties of demand response and wind power

This paper proposes a risk-constrained decision-making approach for a wind power producer participating in the day-ahead market. In the developed model, a flexible demand response trading scheme between the wind power producer and different customers is employed. Through the proposed demand response mechanism, the wind power producer is able to trade demand response resource internally with different customers, and then trade energy externally with the market to increase the expected profit and the wind energy utilization. The uncertainties in the wind power and demand response are modeled by using the information gap decision theory approach from risk averse (robust) and risk-seeking (opportunistic) perspectives. The objective of the robust model is to maximize the robust level while satisfying the desired profit, whereas the opportunistic model aims to evaluate the possibility of achieving windfall profits with favorable uncertainties. The overall offering strategy problem is modeled as a bi-objective mixed integer nonlinear programming, which is linearized by proper techniques and solved efficiently by using the normal boundary intersection technique. In this work, simulation results show that utilizing demand response resource to mitigate wind power deviations can increase a wind power producer's profit and reduce potential risks. In addition, the results demonstrate that the proposed bi-objective optimization approach enables the wind power producer to select appropriate offering decisions with respect to uncertainties.

17 WIND ENERGY↗

DOE OSTI2024

Agent-based modeling for multimodal transportation of CO 2 for carbon capture, utilization, and storage: CCUS-agent

Here, to understand the system-level interactions between the entities in Carbon Capture, Utilization, and Storage (CCUS), an agent-based foundational modeling tool, CCUS-Agent, is developed for a large-scale study of transportation flows and infrastructure in the United States. Key features of the tool include (i) modular design, (ii) multiple transportation modes, (iii) capabilities for extension, and (iv) testing against various system components and networks of small and large sizes. Five matching algorithms for CO 2 supply agents (e.g., powerplants and industrial facilities) and demand agents (e.g., storage and utilization sites) are explored: Most Profitable First Year (MPFY), Most Profitable All Years (MPAY), Shortest Total Distance First Year (SDFY), Shortest Total Distance All Years (SDAY), and Shortest distance to long-haul transport All Years (ACAY). Before matching, the supply agent, demand agent, and route must be available, and the connection must be profitable. A profitable connection means the supply agent portion of revenue from the 45Q tax credit must cover the supply agent costs and all transportation costs, while the demand agent revenue portion must cover all demand agent costs. A case study employing over 5500 supply and demand agents and multimodal CCUS transportation infrastructure in the contiguous United States is conducted. The results suggest that it is possible to capture over 9 billion tonnes (GT) of CO 2 from 2025 to 2043, which will increase significantly to 22 GT if the capture costs are reduced by 40 %. The MPFY and SDFY algorithms capture more CO 2 earlier in the time horizon, while the MPAY and SDAY algorithms capture more later in the time horizon.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

Modelling the deployment of CO₂ storage in U.S. gas-bearing shales

The proliferation of commercial development in U.S. gas-bearing shales helped to drive a twelve-fold increase in domestic gas production between 2000 and 2010, and the nation's gas production rates continue to grow. While shales have long been regarded as a desirable caprock for CCS operations because of their low permeability and porosity, there is increasing interest in the feasibility of injecting CO₂ into shales to enhance methane recovery and augment CO₂ storage. Laboratory work published in recent years observes that shales with adsorbed methane appear to exhibit a stronger affinity for CO₂ adsorption, offering the potential to drive additional CH₄ recovery beyond primary production and perhaps the potential to store a larger volume of CO₂ than the volume of methane displaced. Recent research by the authors on the revenues associated with CO₂-enhanced gas recovery (CO₂-EGR) in gas-bearing shales estimates that, based on a range of EGR response rates, the average revenue per ton of CO₂ for projects managed over both EGR and subsequent storage-only phases could range from $0.50 to $18/tCO₂. While perhaps not as profitable as EOR, for regions where lower-cost storage options may be limited, shales could represent another “early opportunity” storage option if proven feasible for reliable EGR and CO₂ storage. Significant storage potential exists in gas shales, with theoretical CO₂ storage resources estimated at approximately 30-50 GtCO₂. However, an analysis of the comprehensive cost competitiveness of these various options is necessary to understand the degree to which they might meaningfully impact U.S. CCS deployment or costs. This preliminary analysis shows that the degree to which EGR-based CO₂ storage could play a role in commercial-scale deployment is heavily dependent upon the offsetting revenues associated with incremental recovery; modeling the low revenue case resulted in only five shale-based projects, while under the high revenue case, shales accounted for as much as 20 percent of total U.S. storage in the first 20 years of deployment. Interestingly, even in this highest revenue case, there appear to be no negative-cost projects that would be profitable in a no-policy environment as modeled under the assumptions employed. While this reflects a very first look at the potential for shales, it is clear that more laboratory and experimental work are needed to reduce uncertainty in key variables and begin to differentiate and identify high-potential shales for early pilot study.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Evaluation of the economic implications of varied pressure drawdown strategies generated using a real-time, rapid predictive, multi-fidelity model for unconventional oil and gas wells

Experience has suggested that pressure maintenance in hydraulically fractured reservoirs via lower, more sustained production drawdowns may offer improved cumulative recovery and overall resource extraction efficiency compared to more rapid drawdown approaches aimed at generating high initial production. However, given the inherent variability of oil and natural gas markets, operators pursue production strategies that maximize profitability over resource extraction efficiency. This study focuses on evaluating the implications of contrasting pressure drawdown strategies on the long-term production and resulting economics for a real, producing unconventional gas well in the Marcellus Shale of the Appalachian Basin using a techno-economic analysis approach. Our research combines elements of well-specific horizontal well design, production forecasting, equipment sizing and capital cost estimation, operating cost estimation, and revenue and tax calculations. Gas production forecast outlook scenarios were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning workflow and 2) traditional reservoir simulation. A discounted cash flow model was used to evaluate the resulting economic implications for each drawdown scenario—generating output for exploring the coupled effect of factors like the timing and volume of gas production, prevailing economic and market conditions for natural gas, and overall estimated ultimate recovery on profitability metrics such as internal rate of return and net present value. Results show that there is potential to maximize the cumulative gas produced in the specific case study well by employing a lower pressure drawdown. Conversely, the greatest profitability is achieved using rapid drawdown as signified by a small, specific subset of our outlook scenarios. On an averaging basis, we find that the combinations of highest cumulative producing and most profitable scenarios occur under lower drawdowns with long (>40 years) producing timeframes, but require higher relative gas price and lower discounting considerations. Further, the machine learning predictive outlooking capability proved effective for enabling rapid generation of a multitude of scenario forecasts. As a result, a variety of prominent example cases could be generated to strike the balance of greater productivity and economic return given their associated producing features and economic conditions when compared to similar producing scenarios—critical insight that offers improved decision support for unconventional oil and gas operations.

42 ENGINEERING↗

DOE OSTI2021

Utility Regulation Through Legislation: A Cautionary Tale for Legislators, Regulators, Stakeholders, and Utilities

State legislatures in the U.S. are becoming increasingly involved in utility regulation. While state utility legislation can expand regulator authority, for example in the case of renewable energy or energy efficiency standard administration, it can also reduce it. Given the challenging nature of for-profit monopoly regulation, state utility legislation often reduces regulator authority unintentionally. This editorial begins with an examination of the outcomes of state legislation related to baseload generation in the U.S. and the resulting impacts to customers, shareholders, and state economies. The record indicates that utility regulation through state legislation has caused significant economic harm, generally by encouraging for-profit utilities to make riskier investments. A description of more recent state legislation involving distribution grids is presented, along with evidence that increases in grid investment encouraged by state legislation has not improved the reliability of U.S. for-profit utilities. The authors claim that features common to state utility legislation, particularly the requirement that utilities provide investment plans to regulators in advance, practically eliminates cost disallowance risk. This, in turn, reduces regulator authority despite legislative provisions intended to protect consumers. The authors hypothesize that the practical elimination of cost disallowance risk, combined with information and expertise asymmetry among regulators and stakeholders, encourages for-profit monopoly utilities to make grid investments that are not cost effective. The editorial concludes that legislators should avoid utility regulation through legislation, thereby preserving regulator authority, whenever possible. The authors also present their fifth annual Customer Value Ranking of U.S. investor-owned electric utilities. The ranking represents a comparison of the benefits utilities deliver to customers (measured by reliability performance and customer satisfaction) to the costs customers pay (measured by O&M and capital spending per customer).

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗

DOE OSTI2021

How to design a zero-emissions vehicle mandate? Simulating impacts on sales, GHG emissions and cost-effectiveness using the AUtomaker-Consumer Model (AUM)

We report although numerous studies investigate the impacts of policy on zero-emissions vehicle (ZEV) sales, few address the impacts of a ZEV sales mandate and its particular design features. We use the AUtomaker-consumer Model (AUM) to simulate varying designs of a ZEV mandate in Canada's light-duty vehicle sector, requiring 30% ZEV sales by 2030. Design features include: different penalties for non-compliance, different allocations of credits per ZEV type, and the allowance of banking credits across years. For each policy design, we consider impacts on ZEV adoption, GHG emissions, consumer surplus, automaker profits, and overall policy cost-effectiveness. Results demonstrate trade-offs among the policy designs. Generally, ZEV mandate designs that are more effective at increasing ZEV adoption and decreasing GHG emissions also induce larger reductions in consumer surplus and automaker profits. To achieve the ZEV sales goal, simulations show that a higher non-compliance penalty (CDN$ 10k per credit) is needed. ZEV adoption and GHG reductions are also improved by a “One credit per ZEV” scheme, rather than a “California-style” scheme that gives multiple credits per battery-electric vehicle. Further, allowing automakers to bank credits softens the policy impact on profit and increases automaker compliance in initial years. Across these different design combinations, we find that the most cost-effective design ($CDN/tonne mitigated, including consumer surplus and automaker profits impacts) utilizes a $10k non-compliance penalty and “One credit per ZEV” system that allows banking. Our analysis demonstrates the importance of various ZEV mandate design features, and the complex trade-offs involved among various policy goals.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Effectiveness of the food recovery at the retailing stage under shelf life uncertainty: An application to Italian food chains

Highlights: • The food recovery is seen as suitable way to manage food near to its expiry date. • The variability of the products shelf life must be taken into account. • The paper addresses the mathematic modeling of the profit related to food recovery. • The optimal time to withdraw the products is determinant for food recovery. - Abstract: Food losses represent a significant issue affecting food supply chains. The possibility of recovering such products can be seen as an effective way to reduce such a phenomenon, improve supply chain performances and ameliorate the conditions of undernourished people. The topic has been already investigated by a previous paper enforcing the hypothesis of deterministic and constant Shelf Life (SL) of products. However, such a model cannot be properly extended to products affected by uncertainties of the SL as it does not take into account the deterioration costs and loss of profits due to the overcoming of the SL within the cycle time. Thus the present paper presents an extension of the previous one under stochastic conditions of the food quality. Differently from the previous publication, this work represents a general model applicable to all supply chains, especially to those managing fresh products characterized by uncertain SL such as fruits and vegetables. The deterioration costs and loss of profits are included in the model and the optimal time at which to withdraw the products from the shelves as well as the quantities to be shipped at each alternative destination have been determined. A comparison of the proposed model with that reported in the previous publication has been carried out in order to underline the impact of the SL variability on the optimality conditions. The results show that the food recovery strategy in the presence of uncertainty of the food quality is rewarding, even if the optimal profit is lower than that of the deterministic case.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Economic Incentives for Agrivoltaics Systems with Commodity Crops in the Midwestern United States

Declining costs of photovoltaic (PV) technology and rising market and policy incentives are leading to the growing deployment of PV on cropland in the US Midwest, leading to concerns about the displacement of food and feed crop production. Agrivoltaic (AV) technology enables the dual use of land by co-locating PV energy and crop production, potentially reducing land-use competition with crop production. We develop a benefit-cost analysis framework to compare the net economic returns from AV to those with stand-alone PV and crop production on a representative field and show conditions under which AV can be more profitable for both a solar developer and a farmer. We integrate it with a crop and solar energy model to simulate the performance of various field designs and space and height configurations in AV systems to accommodate soybean production with conventional farm equipment under representative conditions in the US Midwest. We find that an AV system with soybean production is less profitable than PV alone for a solar developer due to the high capital costs of raising panel height, and less profitable for a farmer than leasing land for PV due to its adverse effects of shading on crop yield. We discuss the changes in technology and market prices of solar energy and soybeans that are necessary to make the AV system profitable for solar developers and farmers. We show that AV can worsen rather than mitigate the conflict between food crops and solar energy production in the Midwest.

14 SOLAR ENERGY↗

DOE OSTI2025

Game theoretic modeling and optimization of competition and collaboration in dual channel electronic waste supply chains

The rapid growth of electronic waste (e-waste) presents critical challenges for sustainable resource recovery and environmental protection. This study develops a dual-channel closed-loop supply chain (CLSC) model formulated as a hierarchical Stackelberg game, that integrates dynamic pricing and cost-sharing mechanisms to optimize both economic and environmental outcomes. The model explicitly captures strategic interactions between manufacturer-led and third-party recycling channels, accounting for consumer behavior, regulatory incentives, and market competition. Numerical simulations conducted (implemented over a four-iteration horizon using a commercial optimization solver) show that, relative to the baseline equilibrium, manufacturer profit increases from 11.6 thousand USD to 37.9 thousand USD (+226.8%), total recycled volume rises from 7,848 to 7,942 units (+1.2%), and collector profit nearly doubles under cost-sharing, enabling more equitable profit distribution. Furthermore, scenario-based simulations across Sub-Saharan Africa, high-income economies, and emerging Asian industrial countries reveal that infrastructure quality, policy intensity, and labor costs critically shape recycling efficiency and profit allocation. These findings demonstrate that subsidies alone are insufficient to ensure system efficiency. Instead, coordinated strategies that integrate internal incentive alignment with context-sensitive policy support are required. Overall, this study offers a robust framework for designing resilient, efficient, and regionally adaptable e-waste management systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2012

Initial Risk Analysis and Decision Making Framework

Commercialization of new carbon capture simulation initiative (CCSI) technology will include two key elements of risk management, namely, technical risk (will process and plant performance be effective, safe, and reliable) and enterprise risk (can project losses and costs be controlled within the constraints of market demand to maintain profitability and investor confidence). Both of these elements of risk are incorporated into the risk analysis subtask of Task 7. Thus far, this subtask has developed a prototype demonstration tool that quantifies risk based on the expected profitability of expenditures when retrofitting carbon capture technology on a stylized 650 MW pulverized coal electric power generator. The prototype is based on the selection of specific technical and financial factors believed to be important determinants of the expected profitability of carbon capture, subject to uncertainty. The uncertainty surrounding the technical performance and financial variables selected thus far is propagated in a model that calculates the expected profitability of investments in carbon capture and measures risk in terms of variability in expected net returns from these investments. Given the preliminary nature of the results of this prototype, additional work is required to expand the scope of the model to include additional risk factors, additional information on extant and proposed risk factors, the results of a qualitative risk factor elicitation process, and feedback from utilities and other interested parties involved in the carbon capture project. Additional information on proposed distributions of these risk factors will be integrated into a commercial implementation framework for the purpose of a comparative technology investment analysis.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2012

Initial Decision and Risk Analysis

Commercialization of new carbon capture simulation initiative (CCSI) technology will include two key elements of risk management, namely, technical risk (will process and plant performance be effective, safe, and reliable) and enterprise risk (can project losses and costs be controlled within the constraints of market demand to maintain profitability and investor confidence). Both of these elements of risk are incorporated into the risk analysis subtask of Task 7. Thus far, this task has developed a prototype demonstration tool that quantifies risk based on the expected profitability of investments to retrofit carbon capture technology on a stylized 650 MW pulverized coal electric power generator. The prototype is based on the selection of specific technical and financial factors believed to be important determinants of the expected profitability of carbon capture, subject to uncertainty. The uncertainty surrounding the technical performance and financial variables selected thus far is propagated in a model that calculates the expected profitability of investments in carbon capture and measures risk in terms of variability in expected net returns from these investments. Ultimately, this tool will allow for analysis of the financial risks associated with adopting carbon capture technology from the perspective of decision makers at utilities.

Engel, David W.↗

DOE OSTI2015

Improved Method for the Production of Tetrahydrofuran From Biomass

Statement of the Problem: Tetrahydrofuran (THF) is a large volume, commodity chemical used as a basic building block for polyurethane stretch fibers (Spandex), other elastomers, solvents for PVC cements, pharmaceuticals, and coatings. Current THF production requires petroleum-based raw materials and significant energy inputs, representing inefficient use of petroleum resources and placing the U.S. at a manufacturing disadvantage compared to chemical manufacturers in other world areas. Pulp and paper manufacture is one of the largest industries in the U.S., representing about 6% of the manufacturing sector of the U.S. GDP. In recent decades, the pulp and paper industry has struggled with a declining market, poor profitability, and competition from other world areas, leading to the shutdown of many U.S. pulp and paper mills, and loss of U.S. employment. The production of chemicals, such as THF from pulp and paper waste hydrolysate, can provide new technologies and products from pulp and paper manufacture which can enhance industry profitability. The iCARD technology will produce and sell THF from selected pulp and paper byproduct hydrolysates without deleterious effect on ongoing pulp production of the mill. Overall Objective: The overall goal of this Small Business Innovation Research Phase II Project was to complete the research, development, and design tasks required to commercialize an innovative technology using catalytic reactive distillation to achieve process intensification for the manufacture of “green” THF from pulp mill waste hydrolysate. The new technology will benefit the U.S. pulp and paper industry by providing new technologies and products from pulp and paper manufacture, which can enhance industry profitability, and increase industry employment. Development of commercially competitive biomass based technologies will reduce consumption of scarce hydrocarbon resources, and improve long term U.S. competitiveness in global markets. Conclusions: The novel catalytic reactive distillation technology successfully demonstrated the technical and economic feasibility of the manufacture of THF production from dilute aqueous biomass solutions. New catalyst compositions were developed with catalytic activity for conversion of furfural to THF which exceed the activity of conventional catalysts by 2 to 3 orders of magnitude. The feasibility of achieving selectivity to the desired THF product approaching 100% was demonstrated. The novel catalysts also demonstrated catalyst stability far superior to conventional furfural decarbonylation catalysts. The novel technology also demonstrated the ability to produce other valuable commercial products such as tetrahydrofurfuryl alcohol, 1,5-pentanediol, 2-methyl furan, and 2-tetrahydromethylfuran as co-products with THF. Based on the use of waste pulp mill hydrolysate as the biomass raw material source, the new technology will produce THF for a total manufacturing cost less than the raw material cost for the hydrocarbon-based feed stocks for conventional THF production. Commercial Applications: Selective catalytic reactive distillation of pulp mill waste hydrolysate will allow commercial production of “green” THF from biomass feedstocks for the production of “green” elastomer fibers, and industrial solvents. The new technology will benefit the U.S. pulp and paper industry by providing new technologies and products from pulp and paper manufacture, which can enhance industry profitability, and increase industry employment. Development of commercially competitive biomass based technologies will reduce consumption of scarce hydrocarbon resources, and improve long term U.S. competitiveness in global markets.

09 BIOMASS FUELS↗

DOE OSTI2014

Economic Conditions and Factors Affecting New Nuclear Power Deployment

This report documents work performed in support of the US Department of Energy Office of Nuclear Energy’s Advanced Small Modular Reactor (AdvSMR) program. The report presents information and results from economic analyses to describe current electricity market conditions and those key factors that may impact the deployment of AdvSMRs or any other new nuclear power plants. Thus, this report serves as a reference document for DOE as it moves forward with its plans to develop advanced reactors, including AdvSMRs. For the purpose of this analysis, information on electricity markets and nuclear power plant operating costs will be combined to examine the current state of the nuclear industry and the process required to successfully move forward with new nuclear power in general and AdvSMRs in particular. The current electricity market is generally unfavorable to new nuclear construction, especially in deregulated markets with heavy competition from natural gas and subsidized renewables. The successful and profitable operation of a nuclear power plant (or any power plant) requires the rate at which the electricity is sold to be sufficiently greater than the cost to operate. The wholesale rates in most US markets have settled into values that provide profits for most operating nuclear power plants but are too low to support the added cost of capital recovery for new nuclear construction. There is a strong geographic dependence on the wholesale rate, with some markets currently able to support new nuclear construction. However, there is also a strong geographic dependence on pronuclear public opinion; the areas where power prices are high tend to have unfavorable views on the construction of new nuclear power plants. The use of government-backed incentives, such as subsidies, can help provide a margin to help justify construction projects that otherwise may not seem viable. Similarly, low interest rates for the project will also add a positive margin to the economic analysis. In both cases, the profitable price point is decreased, making more markets open to profitable entry. Overall, the economic attractiveness of a nuclear power construction project is not only a function of its own costs, but a function of the market into which it is deployed. Many of the market characteristics are out of the control of the potential nuclear power plant operators. The decision-making process for the power industry in general is complicated by the short-term market volatility in both the wholesale electricity market and the commodity (natural gas) market. Decisions based on market conditions today may be rendered null and void in six months. With a multiple-year lead time, nuclear power plants are acutely vulnerable to market corrections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗