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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.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
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Blind Validation Study of PRICE TruePlanning and SEER-H

Two of the primary parametric costing tools used to estimate the development and production cost of future spacecraft hardware are PRICE TruePlanning by PRICE Systems, and System Estimation and Evaluation of Resources-Hardware (SEER-H) by Galorath. These are standard tools used by NASA and industry to estimate the cost of new aerospace hardware. This study is an independent verification of the accuracy of these tools and was originally published in 2018. The purpose of this presentation is to circulate the findings of that study among the NASA cost community. 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-H 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%. Nine of the twelve mission's actual costs fell within the 80% confidence interval of SEER's probabilistic estimates; however, none of the missions fell within PRICE’s confidence intervals. There were several factors independent of PRICE and SEER-H 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.

SEER↗
DOE OSTI2015

Determinants of the Price of High-Tech Metals: An Event Study

The growing demand for high-tech products has resulted in strong growth in demand for certain minor metals. In combination with production concentrated in China, this caused strong and unpredicted price movements in recent years. As a result, manufacturing companies have to cope with additional risks. However, the detailed reasons for the price development are only partially understood. Therefore, we analyzed empirically which determinants can be assigned to price movements and performed an event study on the high-tech metals neodymium, indium, and gallium. Based on our dataset of news items, we were able to find coinciding events to almost 90% of all price jumps (recall). We showed that if any information about these events occurred with a probability of over 50% there would also be a price jump within 10 days (precision). However, the classical set of price determinants has to be extended for these specific markets, as we found unorthodox factors like holidays or weather that may be indicators for price movements. Therefore, we hope that our study supports industry for instance in performing more informed short-term planning of metals purchasing based on information about specific events.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Modeling and simulation of consumer response to dynamic pricing.

Assessing the impacts of dynamic-pricing under the smart grid concept is becoming extremely important for deciding its full deployment. In this paper, we develop a model that represents the response of consumers to dynamic pricing. In the model, consumers use forecasted day-ahead prices to shift daily energy consumption from hours when the price is expected to be high to hours when the price is expected to be low while maintaining the total energy consumption as unchanged. We integrate the consumer response model into the Electricity Market Complex Adaptive System (EMCAS). EMCAS is an agent-based model that simulates restructured electricity markets. We explore the impacts of dynamic-pricing on price spikes, peak demand, consumer energy bills, power supplier profits, and congestion costs. A simulation of an 11-node test network that includes eight generation companies and five aggregated consumers is performed for a period of 1 month. In addition, we simulate the Korean power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Sensitivity of Western U.S. power system dynamics to droughts compounded with fuel price variability

The energy sector is heavily dependent on surface water availability for reliable electricity generation. Power system operations are usually evaluated through system reliability and economic perspectives, with assumptions based on market prices and normal water availability. As natural gas technologies continue to dominate new thermal capacity, replacing retiring coal and nuclear generators, natural gas price stability becomes increasingly important and the impact of price volatility on grid operations must be understood. Here, we explore the range of Western Electricity Coordinating Council (WECC) regional power operations, which represents the electric interconnection over the western half of the continental United States, using a production cost model driven by different fuel prices and an integrated hydrology model to simulate historical water availability. Our results show system-wide operation costs increasing in drought years or when gas prices increase, as expected. In addition, we found those system-wide increases can be of the same magnitude. Regional analysis of the responses to compounded droughts and price volatility highlight differences in magnitude and direction depending on regional generation technology portfolios. The Pacific Northwest is most sensitive to drought conditions, Southern California is most sensitive to gas prices, and the Desert Southwest is sensitive to both with the same magnitude. This analysis motivates further research in combined hydrology and power system modeling to characterize regional dynamics and associated uncertainties across scales, which is an important underpinning for the development of more resilient energy and water systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A Market Feedback Framework for Improved Estimates of the Arbitrage Value of Energy Storage Using Price-Taker Models

Price-taker (PT) models are often used to assess the potential value or revenue of energy arbitrage opportunities for energy storage in wholesale markets. But as greater amounts of energy storage are deployed on the grid, current PT models fail to predict the effects that energy storage itself can have on market prices. This can lead to an overestimation of the economic value of storage and an inability to capture price suppression. In this paper, we propose the use of a modified PT model to simulate the impact of increased storage deployment on energy prices and the resulting impact on revenue. Our method uses a gradient-boosting regressor to estimate the impact on prices, and we apply our method on historical price data from the PJM and California Independent System Operator wholesale markets. We use this approach to explore possible causes of electricity price suppression that occur from storage capacity additions, which is generally not possible with PT models.

arbitrage↗
DOE OSTI2018

Automatic Responses of Crop Stocks and Policies Buffer Climate Change Effects on Crop Markets and Price Volatility

Climate change has the potential to affect crop prices and price volatility. However, the economic models used in prior assessments largely do not include known, automatic, stabilizing factors. Crop storage can stabilize prices and U.S. crop policy tends to provide support that moves opposite prices. We quantify effects of circa 2050 climate forcing on the inter-annual variability of U.S. Corn Belt corn and soybean yields using statistical crop models and climate scenarios from regional and global climate models. Climate change generally reduces mean yields and increases the inter-annual variability of yields in the Midwestern U.S. Using these yield impacts and an economic model with automatic market stabilizers, we find only modest increases in price volatility. Although individual producers and states are negatively affected by the yield reductions, the aggregate effect for all corn and soybean producer returns can be positive because of price increases. Moreover, agricultural policies based on price levels or revenue variation offset some of the impacts of market variation on farm income. Our results differ from other recent results and temper concerns that increasing climate instability necessarily translates to greater uncertainty about agricultural commodity uses, including as food and biofuels, in the near future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Market Pricing and Settlements Analysis Considering Capacity Sharing and Reserve Substitutions of Operating Reserve Products

Electricity market pricing and settlement are the key signals for real-time dispatch and long-term investment decisions. Regional transmission operators (RTOs) in the U.S. adopt uniform pricing scheme, which is based on the marginal costs of supplying an incremental MW of electric services. The marginal cost of an electric service is highly dependent on the constraints in the pricing models of RTOs. A slight difference in constraint modeling of pricing model on energy and ancillary services could result in drastically different market clearing prices (MCPs), cleared reserve quantities, and associated revenue. RTOs in the U.S. have various market designs and assumptions in ancillary services modeling in capacity sharing and reserve substitutes. This paper examines four combination models of capacity sharing and reserve substitutes and analyzes the associated market implications. The numerical results present that 1) cascading reserve requirements have direct impact on reserve pricing schemes 2) both cascading reserve requirements and sharing capacity have significant impact on reserve MCPs and locational marginal prices, and thus result in drastically different reserve revenue, energy revenue, generation cost, and generation profit.

ancillary services↗
DOE OSTI2013

The Impact of City-level Permitting Processes on Residential Photovoltaic Installation Prices and Development Times: An Empirical Analysis of Solar Systems in California Cities

Business process or “soft” costs account for well over 50% of the installed price of residential photovoltaic (PV) systems in the United States, so understanding these costs is crucial for identifying PV cost-reduction opportunities. Among these costs are those imposed by city-level permitting processes, which may add both expense and time to the PV development process. Building on previous research, this study evaluates the effect of city-level permitting processes on the installed price of residential PV systems and on the time required to develop and install those systems. The study uses a unique dataset from the U.S. Department of Energy’s Rooftop Solar Challenge Program, which includes city-level permitting process “scores,” plus data from the California Solar Initiative and the U.S. Census. Econometric methods are used to quantify the price and development-time effects of city-level permitting processes on more than 3,000 PV installations across 44 California cities in 2011. Results indicate that city-level permitting processes have a substantial and statistically significant effect on average installation prices and project development times. The results suggest that cities with the most favorable (i.e., highest-scoring) permitting practices can reduce average residential PV prices by $0.27–$0.77/W (4%–12% of median PV prices in California) compared with cities with the most onerous (i.e., lowest-scoring) permitting practices, depending on the regression model used. Though the empirical models for development times are less robust, results suggest that the most streamlined permitting practices may shorten development times by around 24 days on average (25% of the median development time). These findings illustrate the potential price and development-time benefits of streamlining local permitting procedures for PV systems.

14 SOLAR ENERGY↗
DOE OSTI2017

Marginal Cost Pricing in a World without Perfect Competition: Implications for Electricity Markets with High Shares of Low Marginal Cost Resources

A common approach to regulating electricity is through auction-based competitive wholesale markets. The goal of this approach is to provide a reliable supply of power at the lowest reasonable cost to the consumer. This necessitates market structures and operating rules that ensure revenue sufficiency for all generators needed for resource adequacy purposes. Wholesale electricity markets employ marginal-cost pricing to provide cost-effective dispatch such that resources are compensated for their operational costs. However, marginal-cost pricing alone cannot guarantee cost recovery outside of perfect competition, and electricity markets have at least six attributes that preclude them from functioning as perfectly competitive markets. These attributes include market power, externalities, public good attributes, lack of storage, wholesale price caps, and ineffective demand curve. Until (and unless) these failures are ameliorated, some form of corrective action(s) will be necessary to improve market efficiency so that prices can correctly reflect the needed level of system reliability. Many of these options necessarily involve some form of administrative or out-of-market actions, such as scarcity pricing, capacity payments, bilateral or other out-of-market contracts, or some hybrid combination. A key focus with these options is to create a connection between the electricity market and long-term reliability/loss-of-load expectation targets, which are inherently disconnected in the native markets because of the aforementioned market failures. The addition of variable generation resources can exacerbate revenue sufficiency and resource adequacy concerns caused by these underlying market failures. Because variable generation resources have near-zero marginal costs, they effectively suppress energy prices and reduce the capacity factors of conventional generators through the merit-order effect in the simplest case of a convex market; non-convexities can also suppress prices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Short-term Electricity Price Forecasting with Constrained Regressors

The volatility of electricity price presents a challenge to market participants as their decision-making process are highly depend on the accuracy of price forecasts. However, there is growing empirical evidence of increasing price volatility and price spikes in electricity markets as a result of variable renewable energy generation, extreme weather events, and other factors. The distribution shift caused by spikes in electricity price data differentiates the forecasting tasks from other renewable energy sources. Moreover, the observations may be compromised by cyberattacks and thus not available in the testing phase. To this end, we propose a Similarity-Enhanced Electricity Decomposition Forecasting model (SEED-Forecaster) to address the missing response problem and spikes capturing in short-term electricity price forecasting. The effectiveness of the proposed framework is tested on real-world electricity price data from California Independent System Operator (CAISO). Numerical results of case studies show that the proposed SEED-Forecsater can enhance forecasting performance, particularly in capturing electricity spikes, even under conditions without regressors during testing stage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Explaining drivers of housing prices with nonlinear hedonic regressions

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A near-term to net zero alternative to the social cost of carbon for setting carbon prices

The social cost of carbon (SCC) is commonly described and used as the optimal CO 2 price. However, the wide range of SCC estimates provides limited practical assistance to policymakers setting specific CO 2 prices. In this study we describe an alternate near-term to net zero (NT2NZ) approach, estimating CO 2 prices needed in the near term for consistency with a net-zero CO 2 emissions target. This approach dovetails with the emissions-target-focused approach that frames climate policy discussions around the world, avoids uncertainties in estimates of climate damages and long-term decarbonization costs, offers transparency about sensitivities and enables the consideration of CO 2 prices alongside a portfolio of policies. We estimate illustrative NT2NZ CO 2 prices for the United States; for a 2050 net-zero CO 2 emission target, prices are US $ \$34$ to US $ \$64$ per metric ton in 2025 and US $ \$77$ to US $ \$124$ in 2030. These results are most influenced by assumptions about complementary policies and oil prices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

On Harmonizing Today’s Regulated Tariffs and Future Dynamic Electricity Pricing

A novel method for harmonizing the advantages of dynamic retail electricity pricing with the protections of regulated electricity tariffs is discussed and demonstrated. The method socializes and protects customers from long-term locational price variability that is unfair to those customers who are, by no fault of their own, served at congested locations on a distribution system. However, the method preserves short-term (e.g., diurnal) price variability that might induce helpful, mitigative responses from retail electricity customers. Because the method causes actual price recovery to track a customer class’s approved, regulated price recovery, the method may remove regulators’ objections to dynamic electricity pricing and thereby hasten adoption of market-based retail electricity pricing and transactive energy systems.

Consumer protection, Demand response, Market resea↗
DOE OSTI2013

Revisiting the Long-Term Hedge Value of Wind Power in an Era of Low Natural Gas Prices

Expanding production of the United States’ vast shale gas reserves in recent years has put the country on a path towards greater energy independence, enhanced economic prosperity, and (potentially) reduced emissions of greenhouse gases and other pollutants. The corresponding expansion of gas-fired generation in the power sector – driven primarily by lower natural gas prices – has also made it easier and cheaper to integrate large amounts of variable renewable generation, such as wind power, into the grid. At the same time, however, low natural gas prices have suppressed wholesale power prices across the nation, making it harder for wind and other renewable power technologies to compete on cost alone – even despite their recent cost and performance improvements. A near-term softening in policy-driven demand from state-level renewable energy mandates, coupled with a possible phase-out of a key federal tax incentive over time, may exacerbate wind’s challenge in the coming years. As wind power finds it more difficult to compete with gas-fired generation on the basis of near-term cost, it will increasingly need to rely on other attributes, such as its “portfolio” or “hedge” value, as justification for inclusion in the power mix. This article investigates the degree to which wind power can still serve as a cost-effective hedge against rising natural gas prices, given the significant reduction in gas prices in recent years, coupled with expectations that prices will remain low for many years to come. It does so by drawing upon a rich sample of long-term power purchase agreements (“PPAs”) between existing wind generators and electric utilities in the U.S., and comparing the contracted prices at which utilities will be buying wind power from these existing projects for decades to come to a variety of long-term projections of the fuel costs of gas-fired generation modeled by the Energy Information Administration (“EIA”).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Demonstration of automated price response in large customers in New York City using Auto-DR and OpenADR

Demand response (DR) – allowing customers to respond to reliability requests and market prices by changing electricity use from their normal consumption pattern – continues to be seen as an attractive means of demand-side management and a fundamental smart-grid improvement that links supply and demand. From October 2011 to December 2013, the Demand Response Research Center at Lawrence Berkeley National Laboratory, the New York State Energy Research and Development Authority, and partners Honeywell and Akuacom, have conducted a demonstration project enabling Automated Demand Response (Auto-DR) in large commercial buildings located in New York City using Open Automated Demand Response (OpenADR) communication protocols. In particular, this project focuses on demonstrating how the OpenADR platform, enabled by Akuacom, can automate and simplify interactions between buildings and various stakeholders in New York State and enable the automation of customers’ price response to yield bill savings under dynamic pricing. In this paper, the cost control opportunities under day-ahead hourly pricing and Auto-DR control strategies are presented for four demonstration buildings; present the breakdown of Auto-DR enablement costs; summarize the field test results and their load impact; and show potential bill savings by enabling automated price response under Consolidated Edison’s Mandatory Hourly Pricing (MHP) tariff. For one of the sites, the potential bill savings at the site’s current retail rate are shown. Facility managers were given granular equipment-level opt-out capability to ensure full control of the sites during the Auto-DR implementation. The expected bill savings ranged from 1.1% to 8.0% of the total MHP bill. The automation and enablement costs ranged from $70 to $725 per kW shed. The results show that OpenADR can facilitate the automation of price response, deliver savings to the customers and opt-out capability of the implementation retains control of the sites by facility managers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Stabilizing the Grid and Reducing Utility Bills Through Price-Responsive Controls for Heat Pump Water Heaters

The electricity grid is facing increasing challenges in cost-effectively balancing supply and demand. These challenges are exacerbated by increased penetration of photovoltaics, which causes mid-day overproduction, and electrification of gas appliances, which increases peak-period electricity demand. Decarbonization requires shifting building loads from fossil-intensive high-cost times to renewable-intensive low-cost times while maintaining quality of service to occupants. Utilities and ISO’s are investigating new ways of incentivizing this load shifting. One promising method is the use of Highly Dynamic Prices (HDPs). HDPs feature continuously changing prices that reflect real-time grid generation and distribution costs and capacity constraints, and thus incentivize consumers to shift their loads. California’s CPUC CalFUSE proposal and Hawaii’s recent changes demonstrate that electricity tariffs are moving towards this model. For this to work however, loads must have the capability to respond to these prices. Heat pump water heaters (HPWHs) are an ideal device for this purpose because their storage tanks decouple delivery of domestic hot water from electricity consumption. The storage enables control strategies that consume midday solar power to increase the energy stored in the tank, then provide evening peak domestic hot water services using the stored energy. Berkeley Lab's CalFlexHub project is pioneering price-driven load flexibility by developing and deploying cost-minimizing controls for many flexible loads - including HPWHs - in response to HDP. Control development is based on simulations using the Flexible Heat Pump Water Heater Performance Predictor which captures the control decisions of the on-board controller in a residential, integrated HPWH . The price-responsive controls a) shift load in ways that consume additional midday solar power to help stabilize the grid and reduce overall emissions, b) ensure that occupants receive equal or better hot water delivery service, and c) minimize the operating cost for each home in the fleet. On the grid level, the resulting shift will reduce utility operating costs and emissions, and can avoid expensive system capacity expansions. The control approach is customized to each home based on typical hot water consumption patterns. HPWH controllers, whether on the device or remotely, will receive a schedule of CTA-2045-B signals or set temperature adjustments customized to the current HDP price schedule and home. Simulation results for a fleet of 148 HPWHs on a summer day in Berkeley, California show cost savings of 29% and high price electricity consumption reductions of 80%, while maintaining full quality of service.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Retail Electricity Price and Cost Trends: 2024 Update [Slides]

Berkeley Lab’s "Retail Electricity Price and Cost Trends" summarizes recent trends in retail electricity price levels and price drivers in the United States. This report is intended to serve as a reference document for the diverse set of decision-makers impacted by changes in retail electricity prices and to provide a factual basis for assessing recent changes in retail electricity prices and key underlying drivers. National, regional, and state trends are reported for 2019 through 2023 using publicly-available data for: -Average retail electricity prices, retail sales, and utility revenues -Utility capital expenditures, operations and maintenance costs, and fuel and purchased power costs -Retail electricity sales impacts from behind-the-meter resources The report also includes qualitative case studies highlighting recent and/or regionally-specific issues contributing significantly to retail electricity price trends

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Real-time Pricing Demand Response in Operations

Abstract—Dynamic pricing schemes have been implemented in commercial and industrial application settings, and recently they are getting attention for application to residential customers. Time-of-use and critical-peak-pricing rates are in place in various regions and are being piloted in many more. These programs are proving themselves useful for balancing energy during peak periods; however, real-time (5 minute) pricing signals combined with automation in end-use systems have the potential to deliver even more benefits to operators and consumers. Besides system peak shaving, a real-time pricing system can contribute demand response based on the locational marginal price of electricity, reduce load in response to a generator outage, and respond to local distribution system capacity limiting situations. The US Department of Energy (DOE) is teaming with a mid-west electricity service provider to run a distribution feeder-based retail electricity market that negotiates with residential automation equipment and clears every 5 minutes, thus providing a signal for lowering or raising electric consumption based on operational objectives of economic efficiency and reliability. This paper outlines the capability of the real-time pricing system and the operational scenarios being tested as the system is rolled-out starting in the first half of 2012.

Demand Response↗