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At least 199 records · Page 11

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NASA NTRSDate not supplied

Preliminary Electric Motor Drivetrain Optimization Studies for Urban Air Mobility Vehicles

Abstract- Electric and hybrid electric aircraft require high performance and reliable electric motor drivetrains. These drivetrains, consisting of a motor, an inverter, a gearbox, and a thermal management system, are highly coupled systems where the design of individual components in the drivetrain will significantly affect the sizing and performance of the other components in the system. In this paper, a preliminary co-optimization tool for electric motor drivetrains for Urban Air Mobility vehicles is presented. An example study with the tool is completed for NASA’s RVLT quadrotor concept vehicle.

Thomas Tallerico↗

NASA NTRSDate not supplied

Preliminary Electric Motor Drivetrain Optimization Studies for Urban Air Mobility Vehicles

Electric and hybrid electric aircraft require high performance and reliable electric motor drivetrains. These drivetrains, consisting of a motor, an inverter, a gearbox, and a thermal management system, are highly coupled systems where the design of individual components in the drivetrain will significantly affect the sizing and performance of the other components in the system. In this paper, a preliminary co-optimization tool for electric motor drivetrains for Urban Air Mobility vehicles is presented. An example study with the tool is completed for NASA’s RVLT quadrotor concept vehicle.

Thomas Tallerico↗

NASA NTRSDate not supplied

Parametric Optimization of Rigid Wheels for Planetary Surface Mobility Applications

Design-Build-Test approaches for spaceflight hardware are time and cost intensive, which can result in suboptimal mechanism designs. Optimization-based approaches that utilize high-fidelity models and physics simulation could overcome these limitations while simultaneously speeding up the mechanical design process and reducing cost. In this work, we present a toolchain that enables the multi-objective optimization of rigid rover wheels for planetary surface mobility applications. The toolchain uses Chrono’s Continuous Representation Model (CRM) functionality to simulate granular soil and performs multi-objective parametric optimization on candidate rover wheels to meet a desired performance criterion. The resulting wheel design is then evaluated experimentally using a single-wheel testbed. We end with a discussion of how the toolchain can be extended to simultaneously co-optimize other system parameters, such as system power consumption and feedback control gains.

Optimization↗

DOE OSTI2020

Contributions of Solar + Storage to Future Power Needs

The costs of solar photovoltaics (PV) have been dropping in recent years, leading to increasing installations of solar PV systems and growing interest in how the technology will impact the electric grid. Additionally, as battery costs decline, there is increasing interest in understanding the benefits and limitations of battery storage for the grid as well as potential co-benefits of coupling battery storage and photovoltaics. This paper presents recent methodological developments to more accurately represent the value and limitations of coupled PV and battery systems in capacity expansion models. These are demonstrated using the Resource Planning Model (RPM), which co-optimizes capacity investments, transmission investments, and reduced-order dispatch through 2045. We simulate the evolution of the generation and transmission system in the Western Interconnection both with and without a coupled PV and battery technology option under two core scenarios - a baseline scenario and a high renewable penetration scenario - coupled with sensitivities assuming low and mid PV and battery cost projections. We analyze the degree to which a variety of configurations of coupled PV + Battery systems are able to meet future electricity needs, under which system conditions this coupled technology becomes beneficial to the electric grid and identify the displaced technologies when coupled solar and storage is introduced into the model. We find that capturing the benefits of co-locating these systems becomes increasingly important as the penetration of solar PV rises, and that in particular it is important to accurately capture the ability of these technologies to shift energy, provide firm capacity, and reduce expected curtailment.

14 SOLAR ENERGY↗

DOE OSTI2020

A numerical study on the sooting tendencies of bio-derived fuels for spark-ignition engines

While more efficient vehicles have been developed over the decades, their performance is limited by the properties of existing fuels. In response, the Co-Optimization of Fuels and Engines Initiative (Co-Optima) under the U.S. Department of Energy (DOE) has developed a rigorous screening approach to identify the most promising biomass-derived blendstocks that are suitable for advanced spark-ignition (SI) engines. A detailed kinetic model has also been developed to predict the combustion properties of the selected blendstocks. This kinetic model has been designed mainly targeting ignition and flame propagation properties, and it has not previously been validated for soot formation. In this work, we numerically predicted the sooting tendency of 20 Co-Optima SI blendstocks using the Co-Optima kinetic model. The sooting tendencies are determined quantitatively using the Yield Sooting Index (YSI) methodology. As shown in Fig. 1, the predicted YSIs show good agreement with measurements, except for 2,5-dimethylfuran. We also quantify the sensitivity of the YSI predictions to aromatic growth reactions and fuel decomposition reactions of the test fuels. It is found that PAH growth reactions have little impact on YSIs for all 20 fuels under investigation. On the other hand, fuel decomposition reactions have a significant influence on the YSI of 2,5-dimethylfuran. Perturbing their reaction rates by a factor of two is shown to lead to 11 % YSI prediction uncertainty.

09 BIOMASS FUELS↗

DOE OSTI2020

EV Grid Integration: Pass Cars and Commercial Fleets

The presentation discusses grid integration of EVs, both light duty vehicles and commercial fleets under these topics - Grid impact analysis for pass cars, Opportunities for smart charging for pass cars, Considerations for commercial fleet electrification, and DER Co-optimization for improved business case.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Machine-Learning-Driven, Site-Specific Weather Forecasting for Grid-Interactive Efficient Buildings: Preprint

Emerging grid-interactive efficient buildings (GEBs) have great potential to provide much-needed demand flexibility to electric grids while fulfilling their own control targets by co-optimizing smart appliances, solar photovoltaics, electric vehicles, and energy storage at buildings. To enable the optimal operation of GEBs, site-specific weather information—such as temperature, solar irradiance, relative humidity, and wind speed—is crucial; however, this information is generally unavailable or expensive to obtain. This paper develops advanced machine learning methods to provide precise weather forecasts for individual building sites using readily available weather station data. Support vector regression and artificial neural networks have been employed to learn the spatiotemporal correlations between the weather conditions at nearby weather stations and the individual building site. The proposed site-specific weather forecasting methods have been validated using 1-year actual weather measurement data collected in the Denver metro area. Results show that the developed machine-learning-driven methods can accurately forecast the temperature at the target building site 1 hour ahead with mean absolute error less than 0.72°C and a 48% improvement over the persistence method. Site-specific weather forecasts will improve the understanding of the microclimate effect and its impact on building energy consumption. This information will drive efficiency upgrades and adjustments of building control strategies to improve energy savings and increase flexibility in building loads.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

Resilient Operation of Power Distribution Systems Using MPC-Based Critical Service Restoration: Preprint

Power distribution systems are more prone to disruptions and cause most power system outages. We propose a service restoration technique to recover the system service (electricity delivery) following an extreme event-triggered substation outage. The proposed technique considers the problem of controlling distributed energy resources (DERs) of a distribution system with the objective of achieving maximum load pick up while satisfying network flow and voltage constraints. The problem is formulated as a model predictive control (MPC), where a linearized optimal power flow (OPF) model is employed to describe the network. The formulation is augmented with a ramping (up) reserve product for the DERs to ensure an upward monotonic load restoration as time evolves. We perform simulations considering the IEEE 13-bus test feeder integrated with wind, solar, microturbine, and energy storage devices. We demonstrate the efficacy of the devised technique in restoring the system loads monotonically, without shedding previously restored loads. We also show the benefit of co-optimization of power and reserve products for DERs on service restoration. In addition, the capability of the technique in regulating nodal voltages and reducing renewable power curtailment is demonstrated.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Resilient Operation of Power Distribution Systems Using MPC-Based Critical Service Restoration

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Efficient Automated Driving Strategies Leveraging Anticipation and Optimal Control

Automated vehicles and advanced driver assistance systems bring computation, sensing, and communication technologies that exceed human abilities in some ways. For example, automated vehicles may sense a panorama all at once, do not suffer from human impairments and distractions, and could wirelessly communicate precise data with neighboring vehicles. Prototype and commercial deployments have demonstrated the capability to relieve human operators of some driving tasks up to and including fully autonomous taxi rides in some areas. The ultimate impact of this technology’s large-scale market penetration on energy efficiency remains unclear, with potential negative factors like road use by empty vehicles competing with positive ones like automatic eco-driving. Fundamentally enabled by historic and look-ahead data, this dissertation addresses the use of automated driving and driver assistance to optimize vehicle motion for energy efficiency. Facets of this problem include car following, co-optimized acceleration and lane change planning, and collaborative multi-agent guidance. Optimal control, especially model predictive control, is used extensively to improve energy efficiency while maintaining safe and timely driving via constraints. Techniques including chance constraints and mixed integer programming help overcome uncertainty and non-convexity challenges. Extensions of these techniques to tractor trailers on sloping roads are provided by making use of linear parameter-varying models. To approach the wheel-input energy eco-driving problem over generally shaped sloping roads with the computational potential for closed-loop implementation, a linear programming formulation is constructed. Distributed and collaborative techniques that enable connected and automated vehicles to accommodate their neighbors in traffic are also explored and compared to centralized control. Using simulations and vehicle-in-the-loop car following experiments, the proposed algorithms are benchmarked against others that do not make use of look-ahead information.

Dollar, Robert Austin↗

DOE OSTI2022

Optimizing DER Deployment for Climate, Health, Resilience, and Energy Bill Benefits Using the REopt Model

Climate change, public health, and resilience to power outages are of critical concern to local governments, federal agencies, and the private sector, and are increasingly motivating investments in distributed energy resources (DERs). However, designing a solar-plus-storage system to co-optimize for climate, health, resilience, and energy bill benefits requires complex trade-offs. To address this need, the National Renewable Energy Laboratory has integrated climate and health impacts of grid-purchased electricity and on-site fuel consumption into the publicly-available REopt web tool and API - a techno-economic model that determines the cost-optimal DER system sizes and dispatch strategy. This presentation will provide an overview of the tool's methodology, datasets, and capabilities, including costing emissions into system sizing and dispatch and setting emissions reductions targets. Results of previous work will demonstrate the use of new emissions accounting capabilities by quantifying the impact of including climate and health costs on the optimal resilient microgrid configurations for a hospital, school, and warehouse across 14 U.S. cities.

climate↗

DOE OSTI2021

Fuel Properties and Kinetics

The Co-Optimization of Fuels and Engines (CoOptima) program is focused on a two-pronged approach to develop new, lower net-carbon fuels and advanced engine technologies in tandem. This effort brings nine national labs and several university and industry partners together to simultaneously identify new fuel candidates and to further engine technology development in the light-, medium-, and heavy-duty vehicle sectors with a focus on fuel economy and emission reduction. The fuel properties and kinetics project is aimed at achieving a better understanding of fuel property kinetics and how they impact engine performance such that more efficient and lower emitting engines can be made a reality. Several promising biofuel candidates were identified through tiered screening approaches and were evaluated utilizing unique bench-scale measurement techniques. Kinetic models were developed to describe autoignition and soot pre-cursor formation mechanisms to identify the most promising candidates that advanced various combustion strategies.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Managing Uncertainty and Flexibility in Day-Ahead Electricity Markets

Net load imbalances from day ahead forecasts can lead to significant grid operations costs and are expected to increase as variable renewable energy adoption grows. We propose a new wholesale market product to manage the risk of net load imbalances called Flexibility Options. This product relies on probabilistic forecasts to estimate flexibility demand and would be co-optimized in the day-ahead market. We also propose stochastic methods that enable DER and flexible load aggregators to participate in flexibility markets while considering the uncertainty in weather and occupant behavior.

day-ahead market↗

DOE OSTI2023

Hydrogen Systems for PERformance-Based Value Stacking

The proposed technology (HYPER-V: HYdrogen Systems for PERformance based Value-stacking) represents a co-optimized control framework to dispatch hydrogen systems for serving complex commercial energy demands. HYPER-V aims at integrating hydrogen systems for a variety of use-cases in all energy markets including (but not limited to) grid services, transportation, and fuels. This presentation is the end-of-the-project merit review describing the outcomes.

grid services↗

DOE OSTI2021

Estimating the value of jointly optimized electric power generation and end use: a study of ISO-scale load shaping applied to the residential building stock

A generation-to-load simulation estimated the impact, in terms of production costs and CO2 emissions, attributable to the joint optimization of electric power generation and flexible end uses to support increasing penetrations of renewable energy. Newly conceived, evaluated, and foundational in developing a U.S. National Standard was a transaction-less yet continuous demand response system based on a day-ahead optimum load shape (OLS) designed to encourage Internet-connected devices to autonomously and voluntarily explore options to favour lowest cost generators - without requiring two-way communications, personally identifiable information, or customer opt-in. Boundary conditions used for model calibration included historical weather, residential building stock construction attributes, home appliance and device empirical operating schedules, prototypical power distribution feeder models, thermal generator heat rates, startup and ramping constraints, and fuel costs. Results of an hourly-based annual case study of Texas indicate a 1/3 reduction in production costs and a 1/5 reduction in CO2 emissions are possible.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Comparing the economic performance of ice storage and batteries for buildings with on-site PV through model predictive control and optimal sizing

Integrating renewable energy and energy storage systems provides a way of operating the electrical grid system more energy efficiently and stably. Thermal storage and batteries are the most common devices for integration. However, it is not clear which integrated storage system performs better in terms of overall economics. Ice storage has low initial and maintenance costs, but there is an efficiency penalty for charging of storage and it can only shift electrical loads associated with building cooling requirements. A battery's round-trip efficiency, on the contrary, is quite consistent and batteries can be used to shift both HVAC and non-HVAC loads. However, batteries have greater initial costs and a shorter life. Finally, this research presents a tool, using model predictive control and optimal sizing, and provides a case study for comparing life-cycle economics of battery and ice storage systems for commercial buildings that have chillers for cooling and an on-site photovoltaic system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

A Systematic Review on Coordinated Restoration Strategies for Power Distribution Grids

Power distribution grids are increasingly exposed to High-Impact Low-Probability (HILP) events, which cause widespread disruptions with severe societal and economic impacts. The growing complexity of modern grids, driven by the integration of distributed energy resources and smart grid technologies, has introduced new challenges to effective service restoration. While significant research has explored individual restoration strategies, such as network reconfiguration and microgrid formation, limited attention has been given to methods in which they can be effectively coordinated. Furthermore, the absence of systematic review papers addressing this issue hampers the development of cohesive restoration frameworks capable of addressing the operational complexities of modern grids. This paper presents a systematic review synthesizing existing knowledge on power grid restoration, identifying key limitations, and highlighting opportunities for coordinated strategies. By addressing research gaps and emphasizing the integration of diverse approaches, this study provides critical insights for advancing grid resiliency and recovery, offering a foundation for future research and practical applications in the face of HILP events.

Systematic review↗

DOE OSTI2022

Graphene-Based Interconnect Exploration for Large SRAM Caches for Ultrascaled Technology Nodes

Graphene-based interconnects are considered promising replacements for traditional copper (Cu) interconnect due to their great electric properties. In this article, an interconnect-memory co- design framework is developed to efficiently optimize various graphene-based interconnect technologies. Four interconnect materials and heterogeneous design schemes are benchmarked against their traditional Cu counterparts to optimize large cache-level SRAM performance in terms of delay and energy per access, energy-delay product (EDP), and energy-delay-area product (EDAP). Here, a large design space exploration is performed based on realistic subarray design and device technology. Various interconnect- and array-level design parameters are studied to quantify the true potential of graphene-based wires for optimal memory performance.

42 ENGINEERING↗