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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Energy Systems Integration Partnerships, NREL + Wyle (Fact Sheet)

This fact sheet describes the partnership between NREL and Wyle Labs at the Energy Systems Integration Facility. Under a research agreement with Wyle Labs, NREL is working with the U.S. Army to complete development and testing of the Consolidated Utility Base Energy (CUBE) System - a power distribution device that delivers power from solar, battery, and diesel generators to loads on forward operating bases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Unlocking Flexibility: Energy Systems Integration [Guest Editorial]

The articles in this special section focus on energy systems integration (ESI). Electric power systems around the world are experiencing great changes, including the retirement of coal and nuclear plants along with a rapid increase in the use of natural gas turbines and variable renewable technologies such as wind and solar. There is also much more use of information and communications technologies to enhance the visibility and controllability of the grid. Flexibility of operation, the ability of a power system to respond to change in demand and supply, is critical to enable higher levels of variable generation. One way to unlock this potential flexibility is to tap into other energy domains. This concept of interconnecting energy domains is called ESI. ESI is the process of coordinating the operation and planning of energy systems across multiple pathways and/or geographical scales to deliver reliable, cost-effective energy services with minimal impact on the environment. Integrating energy domains adds flexibility to the electrical power system. ESI includes interactions among energy vectors and with other large-scale infrastructures including water, transport, and data and communications networks, which are an enabling technology for ESI.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1978

Comparison of fuel-cell and diesel integrated energy systems and a conventional system for a 500-unit apartment

The electrical and thermal energy utilization efficiencies of a 500 unit apartment complex are analyzed and compared for each of three energy supply systems. Two on-site integrated energy systems, one powered by diesel engines and the other by phosphoric-acid fuel cells were compared with a conventional system which uses purchased electricity and on-site boilers for heating. All fuels consumed on-site are clean, synthetic fuels (distillate fuel oil or pipeline quality gas) derived from coal. Purchased electricity was generated from coal at a central station utility. The relative energy consumption and economics of the three systems are analyzed and compared.

Simons, S. N.↗
NASA NTRS1983

Economic competitiveness of fuel cell onsite integrated energy systems

The economic competitiveness of fuel cell onsite integrated energy systems (OS/IES) in residential and commercial buildings is examined. The analysis is carried out for three different buildings with each building assumed to be at three geographic locations spanning a range of climatic conditions. Numerous design options and operating strategies are evaluated and two economic criteria are used to measure economic performance. In general the results show that fuel cell OS/IES's are competitive in most regions of the country if the OS/IES is properly designed. The preferred design is grid connected, makes effective use of the fuel cell's thermal output, and has a fuel cell powerplant sized for the building's base electrical load.

Bollenbacher, G.↗
DOE OSTI2017

Energy Systems Integration: Demonstrating Distribution Feeder Voltage Control

Overview fact sheet about the Smarter Grid Solutions Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) project at the Energy Systems Integration Facility. INTEGRATE is part of the U.S. Department of Energy's Grid Modernization Initiative.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Optimal operations of a nuclear-based integrated energy system: A mixed integer program approach

A nuclear-based integrated energy system (IES), consisting of multiple carbon-free energy generation and conversion technologies to meet heterogeneous end-use demands, offers a promising approach to decarbonizing the U.S. economy. Operating such an IES is challenging due to its complexity and the diverse end-use demands, such as heating and electricity. This paper aims to address the optimal operation of an IES composed of a small modular reactor (SMR), a steam manifold, a balance of plant (BOP), a high-temperature steam electrolysis (HTSE) system, a district heating (DH) network, and electrical grids. We formulate the system’s operation as a mixed integer linear programming (MILP) problem to maximize net revenues from sales of electricity and hydrogen. To evaluate the efficacy of the proposed model, we conduct a 24-hour simulation considering day-ahead (DA) electricity prices from CAISO and a varying DH demand profile with hourly resolution. The simulation results show that our model effectively optimizes the operation by selling electricity during on-peak periods and purchasing electricity for hydrogen production during off-peak periods, while satisfying operating constraints within the IES.

08 HYDROGEN↗
DOE OSTI2017

Energy Systems Integration: Demonstrating Distributed Resource Communications

Overview fact sheet about the Electric Power Research Institute (EPRI) and Schneider Electric Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) project at the Energy Systems Integration Facility. INTEGRATE is part of the U.S. Department of Energy's Grid Modernization Initiative.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Desalination Multi-Effect Evaporator Models in Support of Integrated Energy Systems

The purpose of this work is to contribute to the Integrated Energy Systems (IES) modeling efforts from the Department of Energy with Idaho National Laboratory. The DOE is working on a plug-and-play set of models in an open-source repository called HYBRID that can be used to improve IES modeling capabilities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Energy Systems Integration: Demonstrating the Grid Benefits of Connected Devices

Overview fact sheet about the Electric Power Research Institute (EPRI) and the University of Delaware Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) project at the Energy Systems Integration Facility. INTEGRATE is part of the U.S. Department of Energy's Grid Modernization Initiative.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Off-Road Vehicle Decarbonization and Energy Systems Integration: R&D Gaps and Opportunities

This report summarizes findings from the Off-Road Decarbonization and Energy Systems Integration workshop, hosted by the National Renewable Energy Laboratory (NREL) from March 22-24, 2022. The workshop focused on the importance of collaboration among the off-road vehicle industry and government to address barriers and opportunities for decarbonization. The workshop aligns with priorities of the U.S. Department of Energy (DOE) Vehicle Technologies Office and Hydrogen and Fuel Cell Technologies Office to decarbonize transportation in the agriculture, mining, construction, and military industries. This decarbonization effort is also intended to support original equipment manufacturers, industry associations, technology developers, utilities, and consultants. The sections within this report correspond to the three topic areas covered in the 3-day workshop: a high-level perspective of needs and challenges, vehicle and equipment decarbonization strategies, and energy systems integration opportunities.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Digital Real-Time Simulation and Power Quality Analysis of a Hydrogen-Generating Nuclear-Renewable Integrated Energy System

This paper investigates the challenges and solutions associated with integrating a hydrogen-generating nuclear-renewable integrated energy system (NR-IES) under a transactive energy framework. The proposed system directs excess nuclear power to hydrogen production during periods of low grid demand while utilizing renewables to maintain grid stability. Using digital real-time simulation (DRTS) in the Typhoon HIL 404 model, the dynamic interactions between nuclear power plants, electrolyzers, and power grids are analyzed to mitigate issues such as harmonic distortion, power quality degradation, and low power factor caused by large non-linear loads. A three-phase power conversion system is modeled using the Typhoon HIL 404 model and includes a generator, a variable load, an electrolyzer, and power filters. Active harmonic filters (AHFs) and hybrid active power filters (HAPFs) are implemented to address harmonic mitigation and reactive power compensation. The results reveal that the HAPF topology effectively balances cost efficiency and performance and significantly reduces active filter current requirements compared to AHF-only systems. During maximum electrolyzer operation at 4 MW, the grid frequency dropped below 59.3 Hz without filtering; however, the implementation of power filters successfully restored the frequency to 59.9 Hz, demonstrating its effectiveness in maintaining grid stability. Future work will focus on integrating a deep reinforcement learning (DRL) framework with real-time simulation and optimizing real-time power dispatch, thus enabling a scalable, efficient NR-IES for sustainable energy markets.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Dynamic Modeling of a Fast Spectrum Molten Salt Reactor Integrated Energy System

Liquid-fueled molten salt fast reactors and nuclear-powered integrated energy systems (IESs) have the potential to play a pivotal role in the green energy transition. However, these systems have little to no operating experience. There is therefore increased interest and value in modeling and simulating these systems. The IES dynamic model developed in this work utilizes a lumped-parameter control volume methodology to investigate the behavior of the IES in a variety of accident scenarios. The results provide initial evidence for the potential inherent safety of the advanced reactor because of temperature-dependent reactivity feedback and the efficiency of hydrogen and electricity production at the high temperatures provided by the advanced reactor.

MCFR↗
DOE OSTI2015

The Smart Power Lab at the Energy Systems Integration Facility

Watch how NREL researchers are using the Smart Power Laboratory at the Energy Systems Integration Facility (ESIF) to develop technologies that will help the "smart homes" of the future perform efficiently and communicate effectively with the electricity grid while enhancing occupants' comfort and convenience.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Energy Systems Integration Facility (ESIF) Facility Stewardship Plan: Revision 2.1

The U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), has established the Energy Systems Integration Facility (ESIF) on the campus of the National Renewable Energy Laboratory (NREL) and has designated it as a DOE user facility. This 182,500-ft2 research facility provides state-of-the-art laboratory and support infrastructure to optimize the design and performance of electrical, thermal, fuel, and information technologies and systems at scale. This Facility Stewardship Plan provides DOE and other decision makers with information about the existing and expected capabilities of the ESIF and the expected performance metrics to be applied to ESIF operations. This plan is a living document that will be updated and refined throughout the lifetime of the facility.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

An analysis of physics limited dispatch of nuclear renewable integrated energy systems using deep reinforcement learning and dynamic modeling

Previous approaches to dispatching nuclear integrated energy systems (NIES) have focused on the profitability and flexibility of these systems to operate on energy grids with highly variable pricing. However, due to the complexity involved in modeling and designing these systems, there has been less emphasis on ensuring that these dispatch strategies are physically achievable. It is imperative to develop methods that allow the system to remain within the desired NIES operating conditions and perform this based on realistic limited forecasted information. This research employs next generation artificial intelligence, namely deep reinforcement learning (DRL), and a dynamic system model written in Modelica to find a safe and profitable dispatch strategy for a solar nuclear hybrid design. The DRL agent is shown to find a novel dispatch strategy that manages both power ramping and power levels while respecting operational limits. This DRL-based dispatch is compared to other dispatching strategies including an optimal design solution from mixed integer linear programming (MILP). It is found that incorporating the physics of such a tightly coupled NIES limits the profitability of the MILP-based dispatch strategy. As a result, the MILP solution overestimates the design’s generated revenue. In contrast, DRL significantly reduces the number of breaches of safe operational conditions during energy arbitrage while maintaining profitability. Furthermore, this work paves the way for a more detailed assessment of NIES profitability and could be used to aid operator decisions on future NIES projects.

14 - SOLAR ENERGY↗