Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “profit”

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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources

Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (control-lable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗
DOE OSTI2021

Operation of Natural Gas Pipeline Networks With Storage Under Transient Flow Conditions

Here, we formulate a nonlinear optimal control problem for intraday operation of a natural gas pipeline network that includes storage reservoirs. The dynamics of compressible gas flow through pipes, compressors, reservoirs, and wells are considered. In particular, a reservoir is modeled as a rigid, hollow container that stores gas under isothermal conditions and uniform density, and a well is modeled as a vertical pipe. For each pipe, flow dynamics are described by a coupled partial differential equation (PDE) system in density and mass flux variables, with momentum dissipation modeled using the Darcy–Wiesbach friction approximation. Compressors are modeled as scaling up the pressure of gas between the inlet and outlet. The governing equations for all network components are spatially discretized and assembled into a nonlinear differential-algebraic equation (DAE) system, which synthesizes above-ground pipeline and subsurface reservoir dynamics into a single reduced-order model. We seek to maximize an objective function that quantifies economic profit and network efficiency subject to the flow equations and inequalities that represent operating limitations. The problem is solved using a primal–dual interior point solver, and the solutions are validated in computational experiments and simulations on several pipeline test networks to demonstrate the effectiveness of the proposed methodology.

03 NATURAL GAS↗
DOE OSTI2021

The Impacts of Distinct Flexibility Enhancements on the Value and Dynamics of Natural Gas Power Plant Operations

The rapid increase of renewable generation and its anticipated continued growth requires greater operational flexibility in modern power systems. Previous studies explored the addition of more flexible resources to improve system flexibility, but this may also be achieved at lower cost by enhancing existing power plants via flexibility retrofits. Potential flexibility upgrades to existing thermal plants include modifications that enable faster ramp rates, increase maximum load levels, decrease minimum load levels, and provide faster and lower cost startup operations. In this paper, we focus on improvements to existing natural gas combined cycle generators, and apply a unit commitment model to analyze the impact that each specific upgrade has on their operational dynamics and profitability from both the system perspective and from the asset owner perspective. We show that the value of each type of flexibility improvement varies depending on the capacity factor and usage pattern of the generator before the upgrade, and that the relative benefit of each type of flexibility varies. We demonstrate diminishing returns to increased flexibility when upgrading multiple generators simultaneously. Sensitivity tests for several shares of renewable generation, different generation mixes and different net load realizations confirm the generality of the results shown.

03 NATURAL GAS↗
DOE OSTI2020

A Learning-based Power Management Method for Networked Microgrids Under Incomplete Information

In this paper, we present a model-free reinforcement learning (RL)-based technique to solve the power management problem of networked microgrids (MGs), when the utility has only limited or no detailed knowledge of the MG asset models.The proposed method consists of two levels: at the first level, using an approximate RL strategy and aggregated data, a utility agent determines the locational price signals to maximize its profit under incomplete information on the MGs’ private data behind their points of common coupling (PCC). The proposed RL strategy enables the utility to adapt to changing system conditions and learn from previous experiences, while respecting the data ownership of the MGs. At the second level, the MGs receive the price signals to dispatch their generation/storage assets individually over look-ahead decision time window, while considering power flow constraints. The performance of the proposed RL technique has been verified under realistic operational scenarios.

Zhang, Qianzhi↗
DOE OSTI2022

Offering of Variable Resources in Regulation Markets with Performance Targets: An Analysis

To date, wind and solar power plants have mainly provided energy. Going forward, with the application of appropriate controls, they can offer additional grid services, such as regulating reserves. Additional grid services can present an opportunity for additional value streams to wind and solar power plants. However, the additional value might not be realized when operator-determined performance targets and settlement adjustments for the delivery of regulation capacity are disregarded. Additionally, this article studies the participation of variable resources in centralized regulation markets. We derive analytical results for regulation offers in terms of quantity and price considering that a variable resource does not want to risk being disqualified as a regulation provider, incurring losses, or foregoing more profitable opportunities. Our analysis suggests that U.S. operator-determined performance targets impose an upper bound on the quantity of a variable resource’s regulation offer; and settlement adjustments for the delivery of regulation affect the price at which variable resources offer and the calculations of opportunity costs in case of imperfect regulation delivery.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Multi-layered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. As a result, case studies and a comparative analysis prove the efficacy of the proposed framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Operational Simulation of Multi-Functional Charging Station for Sustainable Transportation

As transportation systems move toward electrification and decarbonization, multifunctional charging stations (MFCS) are emerging as key infrastructure for electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs). This paper presents a simulation model of an MFCS that integrates solar photovoltaic (PV), wind power, battery storage, hydrogen (\mathrm{H}_{2}) production, dual-pressure \mathrm{H}_{2} storage, fuel cells, and dynamic grid interactions. The model simulates daily operations using 5 -minute resolution data to capture realtime variability in renewable energy (RE), demand, and electricity prices. A flexible dispatch algorithm dynamically allocates energy for EV charging, \mathrm{H}_{2} production, storage, and grid transactions while respecting system constraints. Results show that the MFCS effectively prioritizes RE usage, minimizes waste, meets diverse energy demands, and achieves net operational profit. The model serves as a valuable decision-support tool for designing and optimizing integrated clean energy hubs for zero-emission transportation.

Gbadegoye, Jeremiah [University of Tennessee, Knox↗
DOE OSTI2026

Agent-Based Simulation of Price-Demand Dynamics in Multi-Service Charging Station

As the adoption of electric vehicles and hydrogen fuel-cell vehicles grows, understanding how dynamic pricing strategies influence charging and refueling behaviors becomes crucial for optimizing local energy markets. This paper proposes a simulation-based analysis of a hydrogen-electricity integrated charging station that serves both types of vehicles. A multi-agent simulation framework is developed to model the interactions between vehicles and the station, incorporating price- and delay-sensitive behaviors in decision-making. The station can dynamically adjust energy prices, while vehicles optimize their charging or refueling choices based on their utility values. A series of sensitivity analyses are conducted to evaluate how electricity pricing, infrastructure capacity, and waiting behavior impact station performance. Results highlight that moderate electricity prices maximize user participation without sacrificing profit, infrastructure should be right-sized to demand to avoid over- or underutilization, and delay-toleration also affects service outcomes, which may reach the maximum service coverage at the threshold of 45 minutes.

Wang, Xudong [University of Tennessee, Knoxville (↗
DOE OSTI2024

Herbicide‐resistant weed management with robots: A weed ecological–economic model

The heavy reliance on herbicides for weed control has led to an increase in resistant weeds in the United States. Robotic weed control is emerging as an alternative technology for removing weeds mechanically using artificial intelligence. We develop an integrated weed ecological and economic dynamic (I‐WEED) model to examine the biophysical and economic drivers of adopting robotic weed management and simulate the optimal timing and intensity of robotic adoption within and across growing seasons. We specify a cohort‐based weed growth model that relates yield damages to effective weed density and treats the susceptibility of weeds to herbicides as a renewable resource that can be regenerated by using mechanical weeding robots, due to a fitness cost that makes resistant weeds less prolific. Compared to myopic weed management which ignores resistance development, forward‐looking management leads to earlier adoption of robots and treating robots as complements instead of substitutes to herbicides. This weed management results in adopting fewer robots, deploying robots on a smaller portion of the land, higher profitability, and lower yield loss in the long run, relative to myopic management. Counterintuitively, myopic management leads to a lower resistance level through its higher robot adoption intensity. We also find that a lower level of initial weed seed resistance and/or a higher fitness cost result in a higher level of resistance because they create incentives for farmers to delay the adoption of robotic weed control. Our analysis shows the importance of jointly considering the interactions between weed ecology and economics in analyzing the incentives and effects of robotic weed management on weed resistance.

agricultural robotics↗
DOE OSTI2023

A functional trait framework for integrating nitrogen‐fixing cover crops into short‐rotation woody crop systems

Abstract Developing approaches to simultaneously maximize short‐rotation woody crop (SRWC) productivity while minimizing footprints associated with intensive management is imperative to profitable and sustainable bioenergy production systems. Intercropping nitrogen (N)‐fixing cover crops in SRWC systems is an overlooked approach to sustainably intensify SRWC production by increasing N availability using less environmentally costly inputs. Here, we discuss how functional traits (e.g., seasonal activity, lifespan, leaf habit, soil exploration) of cover crops and SRWCs may interact through space and time influencing access to light, water, and nutrients to provide a framework for successful integration of cover crops into SRWCs. Next, we summarize the literature on intercropping forest plantations with N‐fixing cover crops to identity research gaps and outline future research needs and opportunities. And then, using empirical N demand and productivity data from SRWCs and cover crop N inputs from the literature, we illustrate how SRWC leaf habit (conifer evergreens and deciduous hardwoods) would influence successful integration of cover crops and potential N fixation. We estimate that integrating cover crops into SRWCs could supply 27% and 72% of the N demand across a 10‐year rotation for an evergreen and a deciduous hardwood, respectively. These figures suggest these integrated SRWC systems may approach a virtual minimal external N input when other biogeochemical cycles are considered. The guiding principles presented here are grounded in ecological theory and provide a framework for sustainable intensification of forest production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Integration of plant and microbial oil processing at oilcane biorefineries for more sustainable biofuel production

Oilcane—an oil-accumulating crop engineered from sugarcane—and microbial oil have the potential to improve renewable oil production and help meet the expected demand for bioderived oleochemicals and fuels. To assess the potential synergies of processing both plant and microbial oils, the economic and environmental implications of integrating microbial oil production at oilcane and sugarcane biorefineries were characterized. Due to decreased crop yields that lead to higher simulated feedstock prices and lower biorefinery capacities, current oilcane prototypes result in higher costs and carbon intensities than microbial oil from sugarcane. To inform oilcane feedstock development, we calculated the required biomass yields (as a function of oil content) for oilcane to achieve financial parity with sugarcane. At 10 dw% oil, oilcane can sustain up to 30% less yield than sugarcane and still be more profitable in all simulated scenarios. Assuming continued improvements in microbial oil production from cane juice, achieving this target results in a minimum biodiesel selling price of 1.34 [0.90, 1.85] USD∙L –1 (presented as median [5th, 95th] percentiles), a carbon intensity of 0.51 [0.47, 0.55] kg CO 2 e L –1 , and a total biodiesel yield of 2140 [1870, 2410] L ha –1 year –1 . Compared to biofuel production from soybean, this outcome is equivalent to 3.0–3.9 as much biofuel per hectare of land and a 57%–63% reduction in carbon intensity. While only 20% of simulated scenarios fell within the market price range of biodiesel (0.45–1.11 USD∙L –1 ), if the oilcane biomass yield would improve to 25.6 DMT∙ha –1 ∙y –1 (an equivalent yield to sugarcane) 87% of evaluated scenarios would have a minimum biodiesel selling price within or below the market price range.

09 BIOMASS FUELS↗
DOE OSTI2025

Switchgrass ( Panicum Virgatum ) and Miscanthus ( Miscanthus × Giganteus ) Long-Term Yield Patterns Reveal Consistent Productivity Declines

Perennial grasses like switchgrass ( Panicum virgatum ) and miscanthus ( Miscanthus × giganteus ) are expected to supply a substantial amount of the United States bioeconomy's feedstock demand. However, uncertainties around their long-term yields challenge the viability of their potential and limit their wider adoption. To resolve their long-term yield patterns, we analyzed over 200 plantings of switchgrass and miscanthus across Michigan and Wisconsin, USA, measured over 5–15 years. We found a consistent two-phase long-term yield dynamic; during a yield-building phase , peak yields occurred within 4–5 years after planting, followed by a yield-decline phase in which switchgrass and miscanthus lost 30%–47% and 14%–40% of peak yields, respectively. Among the potential drivers of this dynamic and the yield decline, we found that weather conditions had little impact, as the variation across years was not large enough to drive the observed yield differences. Added nitrogen increased peak yields by 10%–20% and attenuated the yield decline by 20%–50%. However, since fertilized stands still showed a yield decline, other factors became limiting as stands aged. This conserved long-term yield dynamic has direct implications on management. A farm-to-gate economic analysis suggests replanting switchgrass and miscanthus 5 and 9 years following their peak yields maximizes profit over a 30-year time horizon. Results call for further management and breeding strategies to mitigate the yield-decline phase, and for reparameterization of global bioenergy models with carbon capture and storage, which may overestimate yields and the economic and environmental benefits of crops grown for bioenergy feedstocks.

bioenergy↗
DOE OSTI2026

Corn and Switchgrass Derived Sustainable Aviation Fuel Carbon Intensity

The production and use of sustainable aviation fuel (SAF) could lower aviation's carbon footprint. Feedstock agricultural management can optimize—or potentially determine—SAF sustainability and profitability, but long-term SAF feedstock comparisons are limited. Life cycle assessments (LCA) are commonly used to estimate the carbon intensity of producing SAF from emerging feedstocks due to the limited availability of long-term, primary field data. We quantified the agricultural phase and well-to-wake (WTWa) carbon intensities of producing SAF from no-till, continuous corn (Zea mays L.) under 120 kg N ha−1 year−1 (120 N corn) and switchgrass (Panicum virgatum L.) under 60 or 120 kg N ha−1 year−1 (60 N and 120 N SWG, respectively) via LCAs based on measured data from a long-term (1998-present) field experiment on marginally productive cropland located in Eastern NE, USA. Measured data included crop productivity, long-term changes in soil organic carbon (SOC) stocks (0–150 cm), annual soil N2O emissions, and field management practices. Both 120 N corn (2078 L ha−1) and 120 N SWG (1752 L ha−1) produced more SAF than 60 N SWG (1229 L ha−1) (p < 0.0001). However, making SAF from corn had greater life cycle GHG emissions (i.e., WTWa carbon intensity) than making SAF from 120 N and 60 N SWG (61.5 > −14.5 and −27.5 g CO2e MJ−1 SAF, respectively; p = 0.0007), primarily due to SOC accrual during crop production. Here, we demonstrate that agricultural management (e.g., crop type, fertilizer use) can determine the sustainably and productivity of producing SAF by using long-term, measured field data in LCAs.

carbon intensity↗
DOE OSTI2021

Eco-evolutionary optimality as a means to improve vegetation and land-surface models

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. Furthermore, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Industry 4.0 and Intelligent Manufacturing Processes: A Review of Modern Sensing Technologies

Digital Manufacturing technologies have quickly become ubiquitous in the manufacturing industry. The transformation commonly referred to as the fourth industrial revolution, or Industry 4.0, has ushered in a wide range of communication technologies, connection mechanisms, and data analysis capabilities. These technologies provide powerful tools to create a more lean, profitable, and data-driven manufacturing processes. In this paper, we review modern communication technologies and connection architectures for Digital Manufacturing and Industry 4.0 applications. An introduction to Cyber-Physical Systems and a review of digital manufacturing trends is followed by an overview of data acquisition methods for manufacturing processes. Numerous communication protocols are presented and discussed for connecting disparate machines and processes. Flexible data architectures are discussed and examples of machine monitoring implementations are provided. Finally, select implementations of these communication protocols and architectures are surveyed with recommendations for future architecture implementations.

42 ENGINEERING↗
DOE OSTI2021

In-Situ Calibrated Digital Process Twin Models for Resource Efficient Manufacturing

The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated in the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, we report an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

42 ENGINEERING↗
DOE OSTI2022

Improved Particle Swarm Algorithm Using Rubik’s Cube Topology for Bilevel Building Energy Transaction

Following the rapid growth of distributed energy resources (e.g., renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as reducing power loss and stabilizing the main power grid. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. Additionally, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush–Kuhn–Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence-based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubik’s Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubik’s Cube topology-based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology-based PSO and its scalability on larger scale applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

BI-LEVEL OPTIMIZATION FOR ELECTRICITY TRANSACTION IN SMART COMMUNITY WITH MODULAR PUMP HYDRO STORAGE

Grid integration of the increasing distributed energy resources could be challenging in terms of new infrastructure investment, power grid stability, etc. To resolve more renewables locally and reduce the need for extensive electricity transmission, a community energy transaction market is assumed with market operator as the leader whose responsibility is to generate local energy prices and clear the energy transaction payment among the prosumers (followers). The leader and multi-followers have competitive objectives of revenue maximization and operational cost minimization. This non-cooperative leader-follower (Stackelberg) game is formulated using a bi-level optimization framework, where a novel modular pump hydro storage technology (GLIDES system) is set as an upper level market operator, and the lower level prosumers are nearby commercial buildings. The best responses of the lower level model could be derived by necessary optimality conditions, and thus the bi-level model could be transformed into single level optimization model via replacing the lower level model by its Karush-Kuhn-Tucker (KKT) necessary conditions. Several experiments have been designed to compare the local energy transaction behavior and profit distribution with the different demand response levels and different local price structures. The experimental results indicate that the lower level prosumers could benefit the most when local buying and selling prices are equal, while maximum revenue potential for the upper level agent could be reached with non-equal trading prices.

Chen, Yang↗