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At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2012

Residential, Commercial, and Utility-Scale Photovoltaic (PV) System Prices in the United States: Current Drivers and Cost-Reduction Opportunities

The price of photovoltaic (PV) systems in the United States (i.e., the cost to the system owner) has dropped precipitously in recent years, led by substantial reductions in global PV module prices. However, system cost reductions are not necessarily realized or realized in a timely manner by many customers. Many reasons exist for the apparent disconnects between installation costs, componentprices, and system prices; most notable is the impact of fair market value considerations on system prices. To guide policy and research and development strategy decisions, it is necessary to develop a granular perspective on the factors that underlie PV system prices and to eliminate subjective pricing parameters. This report's analysis of the overnight capital costs (cash purchase) paid for PVsystems attempts to establish an objective methodology that most closely approximates the book value of PV system assets.

14 SOLAR ENERGY↗

DOE OSTI2021

Robust vehicle routing under uncertainty via branch-price-and-cut

Here, this paper contemplates how branch-price-and-cut solvers can be employed along with the robust optimization paradigm to address parametric uncertainty in the context of vehicle routing problems. In this setting, given postulated uncertainty sets for customer demands and vehicle travel times, one aims to identify a set of cost-effective routes for vehicles to traverse, such that the vehicle capacities and customer time window constraints are respected under any anticipated demand and travel time realization, respectively. To tackle such problems, we propose a novel approach that combines cutting-plane techniques with an advanced branch-price-and-cut algorithm. Specifically, we use deterministic pricing procedures to generate "partially robust" vehicle routes and then utilize robust versions of rounded capacity inequalities and infeasible path elimination constraints to guarantee complete robust feasibility of routing designs against demand and travel time uncertainty. In contrast to recent approaches that modify the pricing algorithm, our approach is both modular and versatile. It permits the use of advanced branch-price-and-cut technologies without significant modification, while it can admit a variety of uncertainty sets that are commonly used in robust optimization but could not be previously employed in a branch-price-and-cut setting.

42 ENGINEERING↗

DOE OSTI2012

A quantum mechanical model for the relationship between stock price and stock ownership

The trade of a fixed stock can be regarded as the basic process that measures its momentary price. The stock price is exactly known only at the time of sale when the stock is between traders, that is, only in the case when the owner is unknown. We show that the stock price can be better described by a function indicating at any moment of time the probabilities for the possible values of price if a transaction takes place. This more general description contains partial information on the stock price, but it also contains partial information on the stock owner. By following the analogy with quantum mechanics, we assume that the time evolution of the function describing the stock price can be described by a Schroedinger type equation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Locational Marginal Pricing in the Campus Power System at the Power Distribution Level

In the development of smart grid at distribution level, the realization of real-time nodal pricing is one of the key challenges. The research work in this paper implements and studies the methodology of locational marginal pricing at distribution level based on a real-world distribution power system. The pricing mechanism utilizes optimal power flow to calculate the corresponding distributional nodal prices. Both Direct Current Optimal Power Flow and Alternate Current Optimal Power Flow are utilized to calculate and analyze the nodal prices. The University of Denver campus power grid is used as the power distribution system test bed to demonstrate the pricing methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Rethinking the Price Formation Problem–Part 1: Participant Incentives under Uncertainty

Operators of organized wholesale electricity markets attempt to form prices in such a way that the private incentives of market participants are consistent with a socially optimal commitment and dispatch schedule. In the U.S. context, several competing price formation schemes have been proposed to address the non-convex production cost functions characteristic of most generation technologies. Here, this paper considers how the design and analysis of price formation policies for non-convex markets are affected by the uncertainty inherent in electricity demand and supply. We argue that by excluding uncertainty, the analytical framework underlying existing policies mischaracterizes the incentives of market participants, leading to inefficient price formation and poor incentives for flexibility. We establish favorable theoretical properties of a new construct, ex ante convex hull pricing , and demonstrate the difference between this idealized benchmark and existing methods on a large-scale test system. Given increased operational uncertainty with a transition to wind and solar generation, distortions caused by poor incentives for flexibility are likely to grow without improved price formation in organized wholesale markets.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Stackelberg Game Based on Supervised Charging Method and Pricing Strategy of Charging Service Providers

The charging of large-scale electric vehicles (EVs) brings about a large new load to the electricity network, while it is also a demand response resource. Smart charging equipment and patterns of charging service will be one of the research focuses at the next stage. Smart charging equipment schedules EV's charging on the basis of electricity price, and this method enables users to hold the right of controlling their own EV's charging procedure. Thus, there exists a Stackelberg game between charging service providers, who set the price, and independent EVs. The marginal charging price, which reflects properties of individual EV, and the pricing strategy of charging service providers can be derived through solving this game problem. A price based charging strategy is proposed, which is suitable for the dynamic electricity price mechanism because of its light communication burden. In a simulation of a charging service provider covering 200 smart devices, this real-time supervised charging strategy shows its advantages of curbing users' payment and guiding the charging load effectively.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2013

Appliance Efficiency Standards and Price Discrimination

I explore the effects of two simultaneous changes in minimum energy efficiency and ENERGY STAR standards for clothes washers. Adapting the Mussa and Rosen (1978) and Ronnen (1991) second-degree price discrimination model, I demonstrate that clothes washer prices and menus adjusted to the new standards in patterns consistent with a market in which firms had been price discriminating. In particular, I show evidence of discontinuous price drops at the time the standards were imposed, driven largely by mid-low efficiency segments of the market. The price discrimination model predicts this result. On the other hand, in a perfectly competition market, prices should increase for these market segments. Additionally, new models proliferated in the highest efficiency market segment following the standard changes. Finally, I show that firms appeared to use different adaptation strategies at the two instances of the standards changing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

The Effects of Market Concentration on Residential Solar PV Prices: Competition, Installer Scale, and Soft Costs

Competition among residential solar photovoltaic (PV) installers may reduce PV price markups and yield lower prices. At the same time, competition may reduce installer experience and opportunities for cost reductions through learning-by-doing and economies of scale. These dynamics suggest that PV non-hardware or 'soft' costs and installed prices depend on the distribution of market shares among installers, also known as market concentration. This study leverages a rich data set of 226,769 residential PV systems to examine the relationship between market concentration, soft costs, and PV prices. The results show that PV prices are lower, on average, in more concentrated markets, i.e., markets with fewer installers or where few installers hold high market share. The study provides evidence that this relationship is non-linear, such that prices are minimized in markets with an optimal balance of the benefits of market concentration and the benefits of competition.

14 SOLAR ENERGY↗

DOE OSTI2026

How Improved Forecasting Can Increase the Bulk Power System Value of Price-Responsive Electric Vehicle Managed Charging

Personal light-duty vehicle (LDV) electric vehicle managed charging (EVMC) can reduce power system costs by better aligning electric vehicle (EV) charging with locations and times of low energy cost or infrastructure use. The need to coordinate charging demand across thousands to millions of vehicles while preserving mobility service is a barrier to realizing the value of EVMC. Price-responsive dispatch mechanisms like time-of-use rates (TOU) and hourly real-time prices (RTP) are attractive compared to direct load control (DLC) because they only require one-way communications and local controls. However, increasing participation in price responsive mechanisms can induce costly-to-serve spikes in load and otherwise increase, rather than decrease, production costs. We quantify the ability of improved EVMC forecasting to sustain savings from price responsive mechanisms beyond the limit of 14% of LDVs actively participating observed in previous work. Perfect forecasting of price-responsive EV load makes TOU and RTP value-competitive with a low-error DLC formulation with up to 27% (within-week flexibility) to 45% or more (within-session flexibility) of LDVs participating in EVMC in an envisioned New England power system with 84% clean energy. Additional costs of implementing DLC should be no more than tens of dollars per vehicle-year if DLC is to be value-competitive with accurately forecast price-responsive EVMC for double-digit percentage shares of LDVs participating.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Relationships between Vehicle Pricing and Features: Data Driven Analysis of the Chinese Vehicle Market

A full-scale understanding of the dynamics of the Chinese vehicle market can benefit stakeholders with respect to rational decision-making and effective long-term investment. This study attempts to discover the common vehicle pricing patterns in the Chinese market by quantifying statistical correlations among critical vehicle features from intrinsic powertrain systems to extrinsic market positioning. The data samples involve almost all passenger vehicle models sold in 2013 to 2019. After comparing multiple statistical methodologies, a log-transformation variant of the multinomial linear regression model was found to be the best one, and the goodness of fit shows that this model can offer stable estimates, which were validated using 2019 market data. The insights achieved are: (1) The price and major performance features of SUVs/crossovers are similar to those of sedans; (2) If all other explicit features remain the same, the price of a Japanese midsize sedan is 62% higher than that of a Chinese midsize sedan, and European midsize vehicles have the highest prices overall. (3) The incremental price of fuel consumption varies by vehicle class and fuel economy. For example, from 30 to 50 MPG, the vehicle price increases by $119 for a Chinese brand sedan vehicle, by $69 for a Chinese brand SUV.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

NREL Price Series Developed for the ARPA-E FLECCS Program

The price data for four regions (CAISO, ERCOT, MISO-W, and PJM-W) are developed using the ReEDS to PLEXOS conversion as described in (Gagnon et al. 2020). The reference ReEDS case chosen is based on the 2020 Standard Scenario Mid-case, which uses the 2020 ReEDS model version (Cole et al. 2020; Ho et al. 2021). All ReEDS model inputs use 2020 Standard Scenarios Mid-case assumptions except for CO2 prices, which are implemented as linearly increasing CO2 price trajectories beginning at $0/tCO2 in 2020 and ending at either $100/tCO2 or $150/tCO2 in 2035 to dive capacity expansion towards a low-carbon system that could support CCS deployment. However, these scenarios also prohibit CCS deployment in this time frame so that resulting price data are not influenced by the deployment and operation of CCS itself. Implementing the ReEDS to PLEXOS conversion tool, PLEXOS is then simulated using the 2035 ReEDS infrastructure for both CO2 price scenarios, with the following model version and setup: PLEXOS Version: 8.2 Solver: Xpress-MP 35.01.01 Mixed integer optimization relative gap 1% System configuration: • Total number of nodes: 134 (consistent with ReEDS balancing areas) • Line losses enforced using piecewise linear approximation • Energy dump was enabled Price data is aggregated to the ISO/RTO level using load-weighted averages. References: Cole, Wesley, Sean Corcoran, Nathaniel Gates, Daniel Mai, Trieu, and Paritosh Das. 2020. “2020 Standard Scenarios Report: A U.S. Electricity Sector Outlook.” NREL/TP-6A20-77442. Golden, CO: National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy21osti/77442.pdf. Gagnon, Pieter, Will Frazier, Elaine Hale, and Wesley Cole. 2020. “Cambium Documentation: Version 2020.” NREL/TP-6A20-78239. National Renewable Energy Lab. (NREL), Golden, CO (United States). https://doi.org/10.2172/1734551. Ho, Jonathan, Jonathon Becker, Maxwell Brown, Patrick Brown, Ilya (ORCID:0000000284917814) Chernyakhovskiy, Stuart Cohen, Wesley (ORCID:000000029194065X) Cole, et al. 2021. “Regional Energy Deployment System (ReEDS) Model Documentation: Version 2020.” NREL/TP-6A20-78195. Golden, CO: National Renewable Energy Laboratory. https://doi.org/10.2172/1788425.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

The Effects of Market Concentration on Residential Solar PV Prices: Competition, Installer Scale, and Soft Costs

Competition among residential solar photovoltaic (PV) installers may reduce PV price markups and yield lower prices. At the same time, competition may reduce installer experience and opportunities for cost reductions through learning-by-doing and economies of scale. These dynamics suggest that PV non-hardware or 'soft' costs and installed prices depend on the distribution of market shares among installers, also known as market structure. This study leverages a rich data set of 226,769 residential PV systems to examine the relationship between market structure, soft costs, and PV prices. The results show that PV prices are lower, on average, in more concentrated markets, i.e., markets with fewer installers or where a subset of installers holds a large market share. The study provides evidence that this relationship is non-linear, such that prices are minimized in markets with an optimal balance of the benefits of market concentration and the benefits of competition.

14 SOLAR ENERGY↗

DOE OSTI2012

Carbon pricing, nuclear power and electricity markets

In 2010, the NEA in conjunction with the International Energy Agency produced an analysis of the Projected Costs of Electricity for almost 200 power plants, covering nuclear, fossil fuel and renewable electricity generation. That analysis used lifetime costs to consider the merits of each technology. However, the lifetime cost analysis is less applicable in liberalised markets and does not look specifically at the viewpoint of the private investor. A follow-up NEA assessment of the competitiveness of nuclear energy against coal- and gas-fired generation under carbon pricing has considered just this question. The economic competition in electricity markets is today between nuclear energy and gas-fired power generation, with coal-fired power generation not being competitive as soon as even modest carbon pricing is introduced. Whether nuclear energy or natural gas comes out ahead in their competition depends on a number of assumptions, which, while all entirely reasonable, yield very different outcomes. The analysis in this study has been developed on the basis of daily data from European power markets over the last five-year period. Three different methodologies, a Profit Analysis looking at historic returns over the past five years, an Investment Analysis projecting the conditions of the past five years over the lifetime of plants and a Carbon Tax Analysis (differentiating the Investment Analysis for different carbon prices) look at the issue of competitiveness from different angles. They show that the competitiveness of nuclear energy depends on a number of variables which in different configurations determine whether electricity produced from nuclear power or from CCGTs generates higher profits for its investors. These are overnight costs, financing costs, gas prices, carbon prices, profit margins (or mark-ups), the amount of coal with carbon capture and electricity prices. This paper will present the outcomes of the analysis in the context of a liberalised electricity market, looking at the impact of the seven key variables and provide conclusions on the portfolio that a utility would be advised to maintain, given the need to limit risks but also to move to low carbon power generation. Such portfolio diversification would not only limit financial investor risk, but also a number of non-financial risks (climate change, security of supply, accidents). (authors)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

A Dynamic Pricing Method to Manage the Impact of EV Charging on the Grid Using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

EV charging, dynamic pricing, grid-informed chargi↗

DOE OSTI2026

How does drought affect residential water demand and price elasticity?

Urban water scarcity is an important social and economic concern, particularly as the intensity, duration, and frequency of droughts is increasing in many regions. We consider whether drought induces changes to water demand and the price elasticity of demand for water that may last beyond a drought’s official end date. If drought shocks prompt long-term changes in water demand behavior, and these changes occur at broad geographic scale, they could have important implications for modeling adaptive responses to water scarcity. We assemble a novel dataset on residential water demand and pricing in the western United States to test empirically for effects of drought on water demand and price elasticity. We perform our analysis with aggregate quantity, price, and drought data, accounting for endogenous prices under increasing-block water tariffs and using both average and marginal water fees in estimating water demand functions. Results are consistent with the hypothesis that households may become less price-sensitive after exposure to drought. However, we find no systematic evidence of long-run, drought-related reductions in water demand, itself.

demand hardening↗

DOE OSTI2025

A dynamic pricing method to manage the impact of EV charging on the grid using RL

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

Potential job creation in the cellulosic biofuel industry: the effect of feedstock price

Abstract A large‐scale biofuel industry based on cellulosic materials would provide many new job opportunities. We model potential direct jobs generated by a cellulosic ethanol industry in the United States. These job projections are estimated via a bottom‐up approach based on the cellulosic feedstock supply chain systems as a function of the price farmers receive for the feedstock. The cellulosic feedstock types involved in the feedstock supply chain system are crop residues, perennial grasses, annual energy crops, and coppiced and non‐coppiced woody crops. Results show that the feedstock transportation sector is where most of the jobs are created, followed by the feedstock production sector. Jobs generated by the feedstock transportation sector account for around one‐third of total new jobs generated, while jobs generated by cellulosic feedstock production account for about one‐quarter of the new jobs. In the United States, most of the new jobs are created in Iowa, Kansas, Missouri, Oklahoma, and Texas. At feedstock prices over US $ 55 per dry ton, more than 100 000 new direct full‐time equivalent jobs are potentially generated. The number of jobs potentially generated by the cellulosic ethanol fuel production system is highly sensitive to the feedstock price. At $60 per ton, a 50% increase in feedstock price, the system generates over 6 times more jobs than it does at a feedstock price of $40 per ton. Many more farmers produce cellulosic feedstock for ethanol production at higher feedstock prices, and thus more ethanol production leads to more job creation. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd

Kim, Seungdo↗

DOE OSTI2022

Adoption of biofuels for marine shipping decarbonization: A long‐term price and scalability assessment

Abstract This study assessed the long‐term annual biofuel production capacity potential and price in the United States and shed light on the prospect of biofuel adoption for marine propulsion. A linear programming model was developed to assist the projections and provide insightful analyses. The projected long‐term (2040) maximum annual capacity of biofuels in the United States is 245 million metric tons (Mt) or 65 billion gallons of heavy fuel oil gallon equivalent (HFOGE) when based on the median feedstock availability. Between 2022 (near‐term) and 2040, the potential biofuel capacity increases by over 40%, attributed to increased feedstock availability. At a price range up to $500/t, biodiesel is the main product, and the annual capacity (12 Mt) is limited to feedstock availability constraints. Biodiesel and corn ethanol are the main biofuels at a price range up to $750/t. At a higher price point (above $750/t), the biofuel types and annual capacities increase substantially (218 Mt per year). Biofuels above this price include gasoline‐, jet‐, and diesel‐range blendstocks, as well as bio‐methanol, bio‐propane, and biogas. This study concludes that the US domestic feedstock availability coupled with advanced conversion technologies can produce substantial amounts of biofuels to achieve a critical mass and be impactful as alternative marine fuels. There is also a need to improve the biofuel price for marine shipping adoption. Policies and economic incentives that provide temporary financial support would help facilitate maritime biofuel adoption. © 2022 Alliance for Sustainable Energy, LLC. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗