Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “PRICES”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Using price-based signals to control plug-in electric vehicle fleet charging

We study decentralized plug-in electric vehicle (PEV) charging control, wherein the system operator (SO) sends price-based signals to a load aggregator (LA) that optimizes charging of a PEV fleet. We study a pricing scheme that conveys price and quantity information to the LA and compare it to a simpler price-only scheme. We prove that the price/quantity-based mechanism can yield a socially optimal solution. We also examine several numerical case studies to demonstrate the superior performance of the price/quantity-based scheme. The price/quantity scheme yields nearly identical PEV charging costs compared to the social optima, whereas the price-only scheme is highly sensitive to the choice of a regularization penalty term that is needed to ensure convergence. As a result, we also show that the time to compute an equilibrium with the price-only mechanism can be up to two orders of magnitude greater than with the price/quantity scheme and can involve 24 times more information exchange between the SO and LA.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Electricity Pricing aware Deep Reinforcement Learning based Intelligent HVAC Control

Recently, deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) has gained a lot of attention due to DRL's ability to optimally control HVAC for minimizing operational cost while maintaining resident's comfort. The success of such DRL-based techniques largely depends on the articulation of the problem in terms of states, actions, and reward function. Inclusion of the electricity pricing information in the problem formulation can play an important role in saving the cost of HVAC operation. However, less attention has been given in the literature on formulating well-crafted state features based on electricity pricing. In this work, we propose an approach for training the DRL model with a specific focus on feature engineering based on electricity pricing. During training, we generate random but sufficiently realistic electricity price signals so that the pre-trained DRL model is robust and adaptive to the dynamic and variable electricity prices. The validation results are encouraging and show the potential of ≈12%-15% savings in the one day cost of HVAC operation, proving the usefulness of including electricity pricing related features as state features.

Kurte, Kuldeep↗
DOE OSTI2014

Deconstructing Solar Photovoltaic Pricing: The Role of Market Structure, Technology, and Policy

Solar photovoltaic (PV) system prices in the United States display considerable heterogeneity both across geographic locations and within a given location. Such heterogeneity may arise due to state and federal policies, differences in market structure, and other factors that influence demand and costs. This paper examines the relative importance of such factors on equilibrium solar PV system prices in the United States using a detailed dataset of roughly 100,000 recent residential and small commercial installations. As expected, we find that PV system prices differ based on characteristics of the systems. More interestingly, we find evidence suggesting that search costs and imperfect competition affect solar PV pricing. Installer density substantially lowers prices, while regions with relatively generous financial incentives for solar PV are associated with higher prices.

14 SOLAR ENERGY↗
DOE OSTI2016

Characteristics of Low-Priced Solar Photovoltaic Systems in the United States

Despite impressive recent cost reductions, there is wide dispersion in the prices of installed solar photovoltaic (PV) systems. We identify the most important factors that make a system likely to be low priced (LP). Our sample consists of detailed characteristics for 42,611 small-scale (< 15 kW) PV systems installed in 15 U.S. states during 2013. Using four definitions of LP systems, we compare LP and non-LP systems and find statistically significant differences in nearly all factors explored, including competition, installer scale, markets, demographics, ownership, policy, and system components. Logit and probit model results robustly indicate that LP systems are associated with markets with few active installers; experienced installers; customer ownership; large systems; retrofits; and thin-film, low-efficiency, and Chinese modules. We also find significant differences across states, with LP systems much more likely to occur in some than in others. Our focus on the left tail of the price distribution provides implications for policy that are distinct from recent studies of mean prices. While those studies find that PV subsidies increase mean prices, we find that subsidies also generate LP systems. PV subsidies appear to simultaneously shift and broaden the price distribution. Much of this broadening occurs in a particular location, northern California, which is worthy of further investigation with new data.

14 SOLAR ENERGY↗
DOE OSTI2015

Methodology for Estimating Purchased Prices of Energy-efficient Motors Internationally and Results for Selected Countries

Motor-driven systems accounts for approximately 60% of manufacturing final electricity use worldwide. Setting standards and designing policies to increase the energy efficiency of the electric motor stocks in a country is a complex process that requires substantial amount of data and analysis. One of the key data needed for this purpose is the purchase price of electric motors in local market conditions. This study was conducted to develop a bottom-up approach to estimate the purchase price of electric motors in different countries/regions for different sizes, number of poles, enclosures, and efficiency levels by adjusting a recent analysis from the U.S to local parameters. The methodology allows for estimates of manufacturer selling prices, along with other costs associated with the distribution channel to calculate purchase prices for different efficiency levels, in different economies. In this report, we provide preliminary results for selected countries/regions in order to illustrate the possible applications of our analysis. Despite the uncertainties due to specific market conditions, this approach can be used by analysts, industry experts, policy makers, and other interested parties to have a more systematic way to estimate the purchase price of electric motors at the country level and use such prices in cost-benefit analyses and cost-effectiveness of higher energy efficiency standards for electric motors.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Impact of Wind, Solar, and Other Factors on Wholesale Power Prices: An Historical Analysis—2008 through 2017

Wholesale power markets have evolved. Some of the most prominent changes over the last decade in the United States include growth in wind and solar, a reduction in the price of natural gas, weakened load growth, and an increase in the retirement of thermal power plants. Here we empirically assess the degree to which wind and solar—among other factors—have influenced wholesale electricity prices. We show that wind and solar have contributed to reductions in overall average annual wholesale electricity prices since 2008, but that natural gas prices have had the largest impact. More notable is that expansion of variable renewable energy has led to significant changes in locational, time of day, and seasonal pricing patterns in some regions. These altered pricing patterns reflect a fundamental shift, and hold important implications for the grid-system value of wind and solar, and for other electric-sector planning and operating decisions.

14 SOLAR ENERGY↗
DOE OSTI2021

2020 Energy Storage Pricing Survey

The annual Energy Storage Pricing Survey (ESPS) series is designed to provide a standardized reference system price for various energy storage technologies across a range of different power and energy ratings. This is an essential first step in comparing systems of the different technologies’ usage costs and total cost of ownership. The final system prices are developed based on data from an extensive set of interviews with representatives across the manufacturing and project development value chain, plus available published data. This information is incorporated into a consistent methodology structure that will allow pricing information to be incorporated at whatever level it was obtained, ranging from component to fully installed system. The ESPS system pricing methodology breaks down the cost of an energy storage system into the following component categories: the storage module; the balance of system; the power conversion system; the energy management system; and the engineering, procurement, and construction costs. By evaluating each of the different component costs separately, a more accurate system cost can be developed that provides internal pricing consistency between different project sizes using the same technology, as well as between different technologies that utilize similar components.

25 ENERGY STORAGE↗
NASA NTRS1982

STS pricing policy

In 1977 NASA published Shuttle Reimbursement Policies for Civil U.S. Government, DOD and Commercial and Foreign Users. These policies were based on the principle of total cost recovery over a period of time with a fixed flat price for initial period to time to enhance transition. This fixed period was to be followed with annual adjustments thereafter, NASA is establishing a new price for 1986 and beyond. In order to recover costs, that price must be higher than the initial fixed price through FY 1985. NASA intends to remain competitive. Competitive posture includes not only price, but other factors such as assured launch, reliability, and unique services. NASA's pricing policy considers all these factors.

Lee, C. M.↗
NASA NTRS2019

Blind Study Validating Parametric Costing Tools PRICE True Planning and SEER-H for NASA Science Missions

Two of the primary parametric costing tools used to estimate the development and production cost of future spacecraft hardware are PRICE True Planning - Space Missions Catalog by PRICE Systems, and SEER-H by Galorath. These are standard tools used by NASA and industry to estimate the cost of new aerospace hardware. However, no independent verification of the accuracy of these tools is publicly available. Both PRICE Systems and Galorath have completed internal validation studies of their parametric cost estimating tools; however, they only provided the results of the studies and did not detail the exact methods used to perform the validation. In the present study, cost estimators used PRICE True Planning and SEER-H to estimate the cost of twelve different past NASA science missions. The estimators were prevented from knowing the actual cost of the missions in an effort to minimize cognitive biases. In the present study, SEER had an average error of 23%, median error of -0.3%, with a standard deviation of 43%. PRICE had an average error of 52%, median error of 50%, and standard deviation of 45%. There were several factors independent of PRICE and SEER which may have affected the accuracy of the results in the present study including: uncertainty in the technical data used for the estimates, the methods used to estimate uncertainty in spacecraft component mass and numbers of prototypes, and the experience of the estimators.

Friz, Paul D.↗
NASA NTRS2018

Blind Study Validating Parametric Costing Tools PRICE TruePlanning and SEER-H for NASA Science Missions

Two of the primary parametric costing tools used to estimate the development and production cost of future spacecraft hardware are PRICE TruePlanning - Space Missions Catalog by PRICE Systems, and SEER-H by Galorath. These are standard tools used by NASA and industry to estimate the cost of new aerospace hardware. However, no independent verification of the accuracy of these tools is publicly available. Both PRICE Systems and Galorath have completed internal validation studies of their parametric cost estimating tools; however, they only provided the results of the studies and did not detail the exact methods used to perform the validation. In the present study, cost estimators used PRICE TruePlanning and SEER-H to estimate the cost of twelve different past NASA science missions. The estimators were prevented from knowing the actual cost of the missions in an effort to minimize cognitive biases. In the present study, SEER had an average error of 23%, median error of -0.3%, with a standard deviation of 43%. PRICE had an average error of 52%, median error of 50%, and standard deviation of 45%. There were several factors independent of PRICE and SEER which may have affected the accuracy of the results in the present study including: uncertainty in the technical data used for the estimates, the methods used to estimate uncertainty in spacecraft component mass and numbers of prototypes, and the experience of the estimators.

Paul D Friz↗
DOE OSTI2024

Assessing Customer Experience and Business Models around Price-to-Device Communication and Smart Control Pathways in CalFlexHub

California is facing three major challenges in electrical grid operation: renewable overgeneration, steep evening ramping, and growing peak demand. The state has identified dynamic retail price response as a key strategy evidenced by CPUC’s Dynamic Rates proceeding and CEC’s Load Management Standards. Furthermore, the CEC launched a $16M “California Load Flexibility Research and Deployment Hub (CalFlexHub)” administered by Berkeley Lab to accelerate price-response flexible load technologies in buildings and EV charging. There are more than 16 laboratory and field demonstration projects in CalFlexHub, each demonstrating innovative automated price-response technologies. CalFlexHub tests various pathways through which hourly price signals and triggered control commands are communicated to load-flexible devices such as smart thermostats, heat pumps, water heaters, and EVs. We identified seven unique communication and control pathways, which involve combinations of third-party cloud, device OEM’s cloud, building central gateway, and local controller in between the price server and the load-flexible devices. It is important for utilities and policy makers to understand the long-term implications of each pathway in designing future programs and creating related policies and mandates for market transformation. We propose an evaluation framework including the following aspects: ● Functionality: connectivity and uptime, resilience, and optimization; ● Customer experience: simplicity in setup, troubleshooting support, continuity, customer choice, first cost, and ongoing cost; ● Business model and scalability: advance interoperability, holistic solution, bridge unique gap, customer base, and value streams and pricing structures. In this paper, we identify emerging business models associated with each communication pathway and discuss their positive features and challenges from the above aspects.

Liu, Jingjing↗
DOE OSTI2020

Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands

With the rapid increase of variable renewable energy sources in power systems, how to manage and price the uncertainty of renewable resources’ power outputs is becoming an urgent issue. Current market designs considering the uncertainties are mainly based on the probabilistic scenario set of demand and renewable energy resources power outputs. This consideration makes market designs vulnerable to three significant challenges when put into practice. First, the accurate probability distribution of renewable generation is hard to obtain in real-time. Second, it is challenging to clear the market timely with many scenarios to guarantee accuracy. Third, generation cost recovery cannot be guaranteed for some scenarios. To overcome these challenges, this paper proposes a locational uncertainty marginal price model to price the uncertainty explicitly based on a scenario-free stochastic market-clearing model. Instead of using the probabilistic scenario set, the uncertainty of renewable energy sources and loads is modeled with distributionally-robust chance constraints. The correlation of uncertainties can be endogenously modeled in both the market-clearing and the locational uncertainty marginal price formation. Furthermore, this paper proves that generation cost recovery, revenue adequacy, and partial market equilibrium can be achieved using the locational uncertainty marginal price model. Numerical results from both the small and large systems simulations validate that the generation cost recovery is maintained no matter the generation participates in uncertainty mitigation or not. The transmission congestion surplus is also allocated appropriately among loads, renewable energy sources, and financial transmission right owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Mexico and U.S. Power Systems Under Variations in Natural Gas Prices

This study examines the impact of natural gas prices on the power systems of Mexico and the United States. For this, we develop an integrated modeling framework by soft linking three different techno-economic bottom-up models of the power and energy systems, one partial equilibrium model of the natural gas sector, and a partial equilibrium model of the Mexican energy sector. Our results show several interesting results: high natural gas prices raise the use of carbon-intensive technologies in the short-term and boost renewable investments at longer time intervals, increasing emissions in earlier periods and reducing them thereafter. Regarding system costs, because of more capital-intensive green power and lower expenditures in raw energy carriers, capital costs rise and operating costs decrease in the long haul. Furthermore, we see an increase in natural gas demand when its price is low, reducing long-term capital and operating costs through cheaper energy inputs in natural gas facilities and a lower share of capital-intensive renewable facilities in the power system. Concerning emissions, low natural-gas prices decrease coal use in the United States, reducing anthropogenic emissions until the last stages of the optimization period. For Mexico, they show heterogeneous results across models. Policymakers can use this study's results to understand the influence of natural gas prices in the Mexican and United States energy sectors.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

A tri-level distribution locational marginal price-based demand response framework

Here, in this paper, we propose a tri-level, nested, two-stage price-based demand response (PBDR) framework that considers distribution locational marginal price (DLMP) as DR enabler between load-serving entities (LSE), demand response providers (DRPs), and customers in the day-ahead distribution market. It enables LSE and customer interactions by using multiple DRPs, positioned in-between, and independently optimizes their objectives. The problem is formulated using linear power flow with approximated power losses and its application in DLMP as DR pricing. The tri-level problem is solved using a nested reformulation & decomposition (R&D) method and tested on the real Indian-108 bus distribution system under various dynamic pricings. Further, the temporal–spatial variations in DLMPs are assessed using fairness criteria. Numerical analyses demonstrate that DLMP applications can effectively improve economic efficiency, and transparency in DR programs valuation with a favorable fairness margin. The results show that DLMP as DR pricing signal induces (0-2) % variation in DLMP for DR participation up to 10 %. Further, it gives over 90 % fairness over temporal–spatial variation for all the customers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A dynamic price model based on supply and demand with application to techno-economic assessments of rare earth element recovery technologies

Rare Earth Elements (REEs) are essential for sustainable energy technologies. Techno-economic Assessments (TEAs) have been conducted to evaluate innovative approaches to recover REEs from waste streams. However, most TEA studies do not consider the price volatility of REEs, which raises concern over the reliability of the analysis in an unpredictable dynamic market. To address this shortcoming, a dynamic price model is proposed that adjusts REE price based upon supply-demand conditions. The dynamic market behavior is simulated and its effect on a TEA is examined via a case study. The results of the dynamic simulation indicate a growing trend of the market price for REE materials/products. Compared to a conventional TEA that does not consider price dynamics, a significant difference in economic performance metrics is identified from the dynamic TEA results. Finally, the proposed dynamic TEA method is not limited to a specific application and can provide insights for evaluating a broad range of technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Estimating Precommercial Heterogeneous Catalyst Price: A Simple Step-Based Method

Developers of novel catalysts often have limited access to information on the costs of their catalytic materials. The R&D and commercialization decisions made by these researchers would benefit from cost estimates that are accurate and directly comparable to the prices of incumbent catalytic materials, but developing such estimates requires detailed knowledge of industrial synthesis methods and their costs. In order to address one critical part of this need for cost information, namely, the capital and operating costs of using catalyst manufacturing process equipment, a simple and researcher-accessible 'step method' is described herein. This method is based on the estimating techniques that contract manufacturers of industrial catalysts use to develop price quotes for their services. It allows the development of an estimate of catalyst synthesis costs by selection of individual processing steps from a list. Data and methods to allow estimation of processing cost via the step method are provided for synthesis quantities of 1-1000 tons. Price estimates for three example materials, including Pt/C, Ni/Al 2 O 3 , and fluid catalytic cracking catalysts, are described in detail and shown to compare favorably (within +/-20%) with market prices for those materials. Lastly, the step method is placed in the context of a forthcoming free and publicly available catalyst cost estimation tool that will incorporate raw materials pricing, dedicated catalyst plants for higher-volume syntheses, spent catalyst value, and other features.

09 BIOMASS FUELS↗
DOE OSTI2021

A Double-Signal Retail Pricing Scheme for Acquiring Operational Flexibility from Batteries

Batteries can provide valuable operational flexibility to facilitate the system efficiency. However, there lacks market environments to effectively harness and monetize the value of these assets. Most existing works apply a profit-oriented single-signal pricing scheme, which mixes the value of energy and flexibility together and may ultimately raise the electricity bill. Therefore, this paper proposes a profit-neutral double-signal retail pricing scheme that distinguishes elastic market players (i.e., batteries) from inelastic market players (i.e., inflexible loads) and quantifies the value of energy and flexibility separately. Experimental results indicate that under the incentive provided by the proposed retail pricing scheme: 1) The system efficiency benefit aligns with benefits to both batteries and inflexible loads; 2) Value of operational flexibility, contributing to improve the energy efficiency, can be transparently priced and fairly allocated among batteries; and 3) The dominant role in which inflexible loads play on determining the market price is avoided.

25 ENERGY STORAGE↗
DOE OSTI2012

Price Projections of Feedstocks for Biofuels and Biopower in the U.S.

The economic availability of biomass resources is a critical component in evaluating the commercial viability of biofuels. To evaluate projected farmgate prices and grower payments needed to procure 295 million dry Mg (325 million dry tons) of biomass in the U.S. by 2022, this research employs POLYSYS, an economic model of the U.S. agriculture sector. A price-run simulation suggests that a farmgate price of $58.42 Mg{sup -1} ($53.00 dry ton{sup -1}) is needed to procure this supply, while a demand-run simulation suggests that prices of $34.56 and $71.61 Mg{sup -1} ($30.00 and $62.00 dry ton{sup -1}) in are needed in 2012 and 2022, respectively, to procure the same supply, under baseline yield assumptions. Grower payments are reported as farmgate price minus resource-specific harvest costs.

09 BIOMASS FUELS↗