Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cost Modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Crystalline Silicon Photovoltaic Module Manufacturing Costs and Sustainable Pricing: 1H 2018 Benchmark and Cost Reduction Road Map

In this paper we provide an overview of the accounting methods and most recent input data used within NREL's bottom-up crystalline silicon (c-Si) solar photovoltaic (PV) module supply chain cost models. The costs accounting framework that is detailed in this paper provides a methodology to prepare bottom-up c-Si PV supply chain cost models in accordance with the U.S. Generally Accepted Accounting Principles (GAAP) and the International Financial Reporting Standards (IFRS). For the polysilicon, ingot, wafer, cell conversion, and module assembly steps of the c-Si supply chain, we outline what is relevant for calculations of the costs of goods sold (consisting of the materials, labor, electricity, maintenance, and depreciation of capital equipment); as well as the additional operating expenses of research and development (R&D); and sales, general, and business administration (S, G, &A). This 2017 benchmark analysis is applied to current state-of-the-art manufacturing sites located within the United States, China, Taiwan, South Korea, Malaysia, the Philippines, and Germany. Primarily due to country-dependent labor and electricity rates, tax rates, and cost-of-capital considerations, we calculate differences in minimum sustainable pricing for all of these countries. In this paper we also include technical descriptions and bottom-up cost model results for several emerging c-Si technologies including larger Czochralski (Cz) ingots, kerfless wafers, reduced silicon utilization by thinner wafers and advanced diamond wire wafering, new encapsulants and backsheets, shingled cells, and the cell architectures passivated emitter and rear cells (PERC), bifacial passivated emitter and rear totally diffused (PERT) cells, bifacial heterojunction intrinsic thin layer (HIT) cells, and interdigitated all back-contact cells (IBC).

14 SOLAR ENERGY↗
NASA NTRS1991

Modeling preparation costs for space missions by using major cost drivers

A model is suggested for making long range planning cost estimates for Deep Space Network (DSN) support of future space missions. The model is a function of major mission cost drivers, such as maintenance and operations, downlink frequency upgrade, uplink frequency upgrade, telemetry upgrade, antenna gain/noise temperature, radiometric accuracy upgrade, radio science upgrade, and very long baseline interferometry. The model is derived from actual cost data from three space missions: Voyager (Uranus), Voyager (Neptune), and Magellan. The model allows one to estimate the total cost and the cost over time of a similar future space mission. The model was back tested against the three projects and gave cost estimates that range from 17 pct. below to 19 pct. above actual mission preparation costs. The model was also compared with two other independent projects: Mariner Jupiter/Saturn (MJS later became Voyager) and Viking. The model gave total preparation cost estimates that range from 15 pct. above to 4 pct. below actual total preparation costs for MJS and Viking, respectively.

Sherif, J. S.↗
DOE OSTI2022

Evaluating power grid model hydropower feasibility with a river operations model

Production cost models (PCMs) simulate dispatch of generators across a large power grid and are used widely by planners to study the reliability of electricity supply. As energy systems transition away from the thermoelectric technologies that have traditionally balanced electricity supply and demand, hydropower and its representation in PCMs is of increasing importance. A limitation of PCMs applied to continental power grids with diverse generation portfolios is that hydropower generation is simulated without full consideration of complex river dynamics, leading to possible misrepresentation of grid flexibility and performance. In addition, data used in PCMs may reflect outdated operating policies. In this paper we propose a hydropower generation feasibility test for PCMs. The approach uses a detailed hydropower model to determine whether the hourly hydropower schedule from a PCM with simplified monthly parameterization can be attained after accounting for realistic river dynamics and operating policies, such as spill requirements and general water movement and balance through a cascade reservoir system. We perform this hydropower generation test for the ‘Big 10’ hydropower system on the Columbia River (part of the Western Interconnect of the United States), revealing 9% overestimation of available hydropower generation in a PCM solution in an average hydrologic year. Our evaluation provides insight on the cost and opportunities for better representing hydropower in PCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A New Model to Determine Installed System Cost and LCOE for ARPA-E's MOSAIC Micro-Concentrator PV Program: Preprint

The US Department of Energy's Advanced Research Projects Agency-Energy (ARPA-E) started a three-year program in December 2015 called Microscale Optimized Solar-cell Arrays with Integrated Concentration (MOSAIC). It consists of 11 teams from across the United States. Its goal is to have project teams leverage expertise from conventional flat-plate photovoltaic (PV) system configuration and concentrated photovoltaic (CPV) module technology to produce a new class of modules for residential, commercial, and utility-scale markets. The intent is to increase performance and facilitate cost-effective deployment of solar power systems across a wide range of geographical locations in part by concentrating both direct and diffuse components of the sun's irradiance. This paper describes a collaboration of ARPA-E and the National Renewable Energy Laboratory (NREL) to develop a bottom-up system cost model for MOSAIC research teams. The model includes an estimate of regional, all-in, CPV installed cost, and levelized cost of energy (LCOE) by using customized inputs parameters, such as module efficiency, cost, and physical dimensions. This techno-economic cost model can help researchers analyze tradeoffs among different design parameters and then prioritize their distinct research opportunities. Moreover, it establishes a standardized cost benchmarking for MOSAIC micro-CPV systems and a normalized comparison against traditional CPV and today's highly competitive flat-plate PV systems. The model is a valuable design tool within the program, helping validate the various technical approaches and compare future deployed system competitiveness versus incumbent technologies in the PV industry.

14 SOLAR ENERGY↗
DOE OSTI2021

Machine Learning Reduced Order Model for Cost and Emission Assessment of a Pyrolysis System

Biomass pyrolysis is a promising approach for producing economic and environmentally-friendly fuels and bioproducts. Biomass pyrolysis experiments show that feedstock properties have a significant impact on product yields and composition. Scientists are developing detailed chemical reaction mechanisms to capture the relationships between biomass composition and pyrolysis yields. These mechanisms can be computationally intensive. In this study, we investigate the use of a machine learning reduced order model (ROM) for assessing the costs and emissions of a pyrolysis biorefinery. Here, we developed a Kriging-based ROM to predict pyrolysis yields of 314 feedstock samples based on the results of a detailed chemical kinetic pyrolysis mechanism. The ROM is integrated into a chemical process model for calculating mass and energy yields in a commercial-scale (2000 tonne/day) biorefinery. The ROM estimated biofuel yields of 65 to 130 gallons per ton of dry biomass. This results in biofuel minimum fuel-selling prices of $2.62 to $5.43 per gallon and emissions of -13.62 to 145 kg of CO 2 per MJ. The ROM achieved an average mean square error of 1.8e-20 and a mean absolute error of 0.53%. These results suggest that ROMs can facilitate rapid feedstock screening for biorefinery systems.

09 BIOMASS FUELS↗
NASA NTRS1986

Study of selected tether applications in space, phase 3, volume 2

The results of a Phase 3 study of two Selected Tether Applications in Space (STAIS); deorbit of a Shuttle and launch of an Orbital Transfer Vehicle (OTV), both from the space station using a tether were examined. The study objectives were to: perform a preliminary engineering design, define operational scenarios, develop a common cost model, perform cost benefits analyses, and develop a Work Breakdown Structure (WBS). Key features of the performance analysis were to identify the net increases in effective Shuttle cargo capability if tethers are used to assist in the deorbit of Shuttles and the launching of the OTVs from the space station and to define deployer system designs required to accomplish these tasks. Deployer concepts were designed and discussed. Operational scenarios, including timelines, for both tethered and nontethered Shuttle and OTV operations at the space station were evaluated. A summary discussion of the Selected Tether Applications Cost Model (STACOM) and the results of the cost benefits analysis are presented. Several critical technologies needed to implement tether assisted deployment of payloads are also discussed. Conclusions and recommendations are presented.

Source record↗
NASA NTRS1977

A life cycle cost economics model for projects with uniformly varying operating costs

A mathematical model is developed for calculating the life cycle costs for a project where the operating costs increase or decrease in a linear manner with time. The life cycle cost is shown to be a function of the investment costs, initial operating costs, operating cost gradient, project life time, interest rate for capital and salvage value. The results show that the life cycle cost for a project can be grossly underestimated (or overestimated) if the operating costs increase (or decrease) uniformly over time rather than being constant as is often assumed in project economic evaluations. The following range of variables is examined: (1) project life from 2 to 30 years; (2) interest rate from 0 to 15 percent per year; and (3) operating cost gradient from 5 to 90 percent of the initial operating costs. A numerical example plus tables and graphs is given to help calculate project life cycle costs over a wide range of variables.

Remer, D. S.↗
NASA NTRS1983

Cost estimation model for advanced planetary programs, fourth edition

The development of the planetary program cost model is discussed. The Model was updated to incorporate cost data from the most recent US planetary flight projects and extensively revised to more accurately capture the information in the historical cost data base. This data base is comprised of the historical cost data for 13 unmanned lunar and planetary flight programs. The revision was made with a two fold objective: to increase the flexibility of the model in its ability to deal with the broad scope of scenarios under consideration for future missions, and to maintain and possibly improve upon the confidence in the model's capabilities with an expected accuracy of 20%. The Model development included a labor/cost proxy analysis, selection of the functional forms of the estimating relationships, and test statistics. An analysis of the Model is discussed and two sample applications of the cost model are presented.

Spadoni, D. J.↗
DOE OSTI2019

Chapter 9: Cost of Wind Energy Modeling

This chapter focuses on considerations made when modeling cost of energy to be used in any of the abovementioned applications. The level of fidelity required to model each of the four, primary cost of energy elements depends on the characteristics of the wind power plant under consideration. Some 'top-down' models utilize empirical data to reflect currently installed wind power plants, while physics -based, or 'bottom up,' models may be implemented to estimate aspects of future wind-power-plant technology. Regardless of the ultimate application of cost of energy modeling, it is important to carefully assess the wind-power-plant characteristics to determine the type of model and the level of fidelity required to represent each of the four cost of energy elements.

17 WIND ENERGY↗
DOE OSTI2019

Modeling the Performance and Cost of Lithium-Ion Batteries for Electric-Drive Vehicles, Third Edition

This Manual details the Battery Performance and Cost model (BatPaC) developed at Argonne National Laboratory for lithium-ion battery packs used in automotive transportation. The model designs the battery for a specified power, energy, and type of vehicle battery. The cost of the designed battery is then calculated by accounting for every step in the lithium-ion battery manufacturing process. The assumed annual production level directly affects each process step. The total price to vehicle manufacturers calculated by the model represent both our estimates for the present-day costs and projected costs for up to ten years in the future, depending upon the designated scale of production. BatPaC assumes that future plants with production volumes of up to 500,000 EV batteries per year would be highly automated resulting in lower capital equipment and labor costs per unit produced than present day plants that have lower production volumes. All costs are calculated in US$ for the year of the estimate with all input costs for that same year. BatPaC is the only publicly available model of which we are aware that performs a bottom-up lithium-ion battery design and cost calculation. The original model and Manual were publicly peer-reviewed by battery experts assembled by the U.S. Environmental Protection Agency. This revised model and Manual include changes made in response to the comments received from the users; most of the changes were made over a number of years and are summarized here. The purpose of the report is to document the equations and assumptions from which the model has been created. A user of the model will be able to recreate the calculations and perhaps more importantly, understand the driving forces for the results. Instructions for use and illustrations of the model results are also presented. Almost every variable in the calculation may be changed by the user to represent a system different from the default values pre-entered into the program. The distinct advantage of using a bottom-up cost and design model is that the entire power-to-energy space may be traversed to examine the correlation between performance and cost. The BatPaC model accounts for the physical limitations of the electrochemical processes within the battery. Thus, unrealistic designs are penalized in energy density and cost, unlike cost models based on linear extrapolations. Additionally, the consequences on cost and energy density from changes in cell capacity, parallel cell groups, and manufacturing capabilities are easily assessed with the model. New proposed materials may also be examined to translate bench-scale values to the design of full-scale battery packs providing realistic energy densities and prices to the original equipment manufacturer.

25 ENERGY STORAGE↗
NASA NTRS1981

Deep space network software cost estimation model

A parametric software cost estimation model prepared for Deep Space Network (DSN) Data Systems implementation tasks is presented. The resource estimation model incorporates principles and data from a number of existing models. The model calibrates task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit DSN software life cycle statistics. The estimation model output scales a standard DSN Work Breakdown Structure skeleton, which is then input into a PERT/CPM system, producing a detailed schedule and resource budget for the project being planned.

Tausworthe, R. C.↗
NASA NTRS1981

Deep space network software cost estimation model

A parametric software cost estimation model prepared for Jet PRopulsion Laboratory (JPL) Deep Space Network (DSN) Data System implementation tasks is described. The resource estimation mdel modifies and combines a number of existing models. The model calibrates the task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software life-cycle statistics.

Tausworthe, R. C.↗
DOE OSTI2024

Hydrogen Production Cost with Anion Exchange Membrane Electrolysis

Rigorous stakeholder-vetted techno-economic analysis was performed to assess the cost of hydrogen (H 2 ) produced using state-of-the-art Anion Exchange Membrane (AEM) electrolysis. Projected high-volume, untaxed and unsubsidized levelized cost of hydrogen (LCOH)1 range from 2020 $\$$1.78 to $\$$3.68/kg H 2 depending on technology year, process design, and electrolyzer project scale, assuming an electricity price of $\$$0.03/kWh and a capacity factor of 97%. The total installed capital cost for an AEM electrolysis plant was estimated from bottom-up stack and process plant cost models. The stack cost model accounts for manufacturing equipment, equipment maintenance, material, tooling, cycle time, yield, labor, utilities and general overhead. The process plant cost model accounts for purchased equipment, installation costs, site preparation, and general overhead costs. For this study, the AEM electrolysis plant is assumed to be a stick-built, greenfield project developed by an engineering, procurement, and construction (EPC) firm with electrolysis stacks purchased directly from an electrolysis stack manufacturer. The price of the electrolysis stacks is based on a bottom-up cost assessment with business markup for the electrolysis company fabricator. Methods from the Hydrogen Analysis (H2A) production model, a peer-reviewed national laboratory-developed discounted cash flow (DCF) model, were used to calculate the production LCOH in 2020 $\$$/kg H 2 . The baseline electricity price case ($\$$0.03/kWh) corresponds to average wholesale electricity prices currently possible in U.S. markets with plentiful wind. Similar low-cost electricity pricing is possible from solar Power Purchase Agreements (PPA) although these prices are typically limited by renewable energy capacity factors.

08 HYDROGEN↗
NASA NTRS1987

The human role in space (THURIS) applications study. Final briefing

The THURIS (The Human Role in Space) application is an iterative process involving successive assessments of man/machine mixes in terms of performance, cost and technology to arrive at an optimum man/machine mode for the mission application. The process begins with user inputs which define the mission in terms of an event sequence and performance time requirements. The desired initial operational capability date is also an input requirement. THURIS terms and definitions (e.g., generic activities) are applied to the input data converting it into a form which can be analyzed using the THURIS cost model outputs. The cost model produces tabular and graphical outputs for determining the relative cost-effectiveness of a given man/machine mode and generic activity. A technology database is provided to enable assessment of support equipment availability for selected man/machine modes. If technology gaps exist for an application, the database contains information supportive of further investigation into the relevant technologies. The present study concentrated on testing and enhancing the THURIS cost model and subordinate data files and developing a technology database which interfaces directly with the user via technology readiness displays. This effort has resulted in a more powerful, easy-to-use applications system for optimization of man/machine roles. Volume 1 is an executive summary.

Maybee, George W.↗
DOE OSTI2015

Manufacturing Cost Levelization Model – A User’s Guide

The Manufacturing Cost Levelization Model is a cost-performance techno-economic model that estimates total large-scale manufacturing costs for necessary to produce a given product. It is designed to provide production cost estimates for technology researchers to help guide technology research and development towards an eventual cost-effective product. The model presented in this user’s guide is generic and can be tailored to the manufacturing of any product, including the generation of electricity (as a product). This flexibility, however, requires the user to develop the processes and process efficiencies that represents a full-scale manufacturing facility. The generic model is comprised of several modules that estimate variable costs (material, labor, and operating), fixed costs (capital & maintenance), financing structures (debt and equity financing), and tax implications (taxable income after equipment and building depreciation, debt interest payments, and expenses) of a notional manufacturing plant. A cash-flow method is used to estimate a selling price necessary for the manufacturing plant to recover its total cost of production. A levelized unit sales price ($ per unit of product) is determined by dividing the net-present value of the manufacturing plant’s expenses ($) by the net present value of its product output. A user defined production schedule drives the cash-flow method that determines the levelized unit price. In addition, an analyst can increase the levelized unit price to include a gross profit margin to estimate a product sales price. This model allows an analyst to understand the effect that any input variables could have on the cost of manufacturing a product. In addition, the tool is able to perform sensitivity analysis, which can be used to identify the key variables and assumptions that have the greatest influence on the levelized costs. This component is intended to help technology researchers focus their research attention on tasks that offer the greatest opportunities for cost reduction early in the research and development stages of technology invention.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1977

A life cycle cost economics model for automation projects with uniformly varying operating costs

The described mathematical model calculates life-cycle costs for projects with operating costs increasing or decreasing linearly with time. The cost factors involved in the life-cycle cost are considered, and the errors resulting from the assumption of constant rather than uniformly varying operating costs are examined. Parameters in the study range from 2 to 30 years, for project life; 0 to 15% per year, for interest rate; and 5 to 90% of the initial operating cost, for the operating cost gradient. A numerical example is presented.

Remer, D. S.↗
NASA NTRS1992

Modeling the Deep Space Network costs for future space missions by using major cost drivers

This paper develops a cost model to do long range planning cost estimates for Deep Space Network (DSN) support of future space missions. The model is a function of eight major mission cost drivers such as uplink frequency upgrade, telemetry upgrade, and antenna gain/noise temperature upgrade. This paper focuses on the costs required to modify and/or enhance the DSN to prepare for supporting future space missions. Two models are derived from actual cost data from three space missions: Voyager (Uranus), Voyager (Neptune), and Magellan. Model A makes it possible to estimate the total cost, and Model B makes it possible to estimate the time profile cost and total cost for DSN support of a similar future space mission. The models were back tested against the total cost and the time profile cost for DSN support of the three projects, and gave cost estimates which range from 17 percent below to 19 percent above actual costs for Model A, and 22.5 percent below to 17.5 percent above for Model B. The total cost Model A was also applied to estimate the total costs for DSN support of two other independent projects: Mariner Jupiter/Saturn (MJS later became Voyager), and Viking. This model gave total cost estimates for DSN support which range from 15 percent to 4 percent above actual total costs for MJS and Viking, respectively.

Remer, Donald S.↗