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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evaluating the resilience benefits of marine energy in microgrids

Marine energy resources could promote clean energy and resilience of coastal and island microgrids, and thus, these applications are a key future market for marine energy development. To demonstrate these benefits, this paper illustrates how inclusion of wave resources into energy resilience solutions can improve overall grid efficiency and sustainability, as well as maintain electricity supply during grid outages. The paper describes a case study evaluation of the potential to add wave energy to the Moloka’i grid as Hawaii strives to meet a 100% clean energy target. The Microgrid Component Optimization for Resilience tool is used to simulate operation in off- grid conditions and size different combinations of wave, solar photovoltaic (PV), wind, storage, and fuel resources required to meet resilience objectives. This research in- vestigates how including wave resources in a microgrid contributes to reducing biofuel generation, producing a zero-greenhouse gas emission profile, and avoiding the over-sizing of PV and battery systems to accommodate periods of unavailability or high demand. Insight from this paper supports the value proposition of wave resources for future markets and informs the relationship between marine generators and microgrids or isolated grids.

Newman, Sarah F.↗
DOE OSTI2022

Cyber-Resilient Design Methodology for Microgrids

Recent advancement in tools has helped with microgrid design, development, planning and operation. Microgrids offer a unique application based on users with different requirements for tools. The process of designing, constructing, commissioning, and assessing a microgrid is not always straightforward due to these distinct requirements. Additionally, metrics are needed for performance evaluation. This panel will offer an overview and description of tools that helps with microgrid design, construction, planning, operation, cyber security, and metrics-driven performance assessment driven by multiple diverse applications and use cases.

CCE↗
DOE OSTI2022

Transient Stability Study of a Real-World Microgrid with 100% Renewables

This paper performs a transient stability study of a real-world microgrid that can operate with 100% renewables to better understand the stability and reliability of the microgrid under various dynamic scenarios. In particular, the operation of multiple grid-forming (GFM) and grid-following (GFL) inverters in such a power system is not well understood under dynamic operation conditions, such as islanding and black start; therefore, in this paper, an electromagnetic transient model of the microgrid is developed to investigate the stability of the system under various dynamic operating conditions and to identify potential reliability risks. The PSCAD/EMTDC simulation with the high-fidelity model provides helpful insights into the optimal operation modes of GFM and GFL inverters as well as the stability and reliability of the microgrid. It can also inform field deployment in terms of inverter control parameters and coordination as well as the expected performance of black start and unplanned islanding.

black start↗
DOE OSTI2021

A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids

We propose a neural Lyapunov approach to assessing transient stability in power electronic-interfaced microgrid interconnections. The problem of transient stability assessment is cast as one of learning a neural network-structured Lyapunov function in the state space. Based on the function learned, a security region is estimated for monitoring the security of interconnected microgrids in real-time operation. The efficacy of the approach is tested and validated in a grid-connected microgrid and a three-microgrid interconnection. A comparison study suggests that the proposed method can achieve a less conservative characterization of the security region, as compared with a conventional approach.

Huang, Tong↗
DOE OSTI2022

Cyber-Physical Security and Resiliency Analysis Testbed for Critical Microgrids with IEEE 2030.5: Preprint

IEEE 2030.5, also known as the Common Smart Inverter Standard (CSIP) is a protocol that specifies the interface between the end user and the smart grid. This standard was proposed recently, and provides many functions which if implemented incorrectly might lead to vulnerabilities. This paper proposes a cyber-physical microgrid testbed using OpenDSS and IEEE 2030.5 that can be used to study the performance of the CSIP protocol various scenarios. For critical microgrid installations, it is essential that the critical loads are served in spite of multiple contingencies. A resiliency analysis is performed for a military microgrid to study its performance and the results are analyzed.

CVSS↗
DOE OSTI2020

Networked Microgrids for Grid Resilience, Robustness, and Efficiency: A Review

Networked microgrids (NMGs) are clusters of microgrids that are physically connected and functionally interoperable. The massive and unprecedented deployment of smart grid technologies, new business models, and involvement of new stakeholders enable NMGs to be a conceptual operation paradigm for future distribution systems. Much work needs to be done, however, to enable NMGs to achieve seamless coordination, including physical, communication, and functional integration. In this paper, we review and summarize the state-of-the-art methodologies for operation and control of NMGs. Furthermore, we also specifically discuss the notion of dynamic boundaries for advanced microgrid applications. In addition, we introduce the opportunities, challenges, and possible solutions regarding NMGs for improving grid resilience, robustness, and efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Multi-layered Resilient Microgrid Networks

Distribution grid resilience, especially when the grid is under high penetration of Distributed Energy Resources (DER), e.g. PV and energy storage, is essential to ensure the quality of electric power services. The resiliency aspect is particularly important for critical loads that typically include hospitals, police/fire stations, and national security facilities. Networks of multiple interconnected reconfigurable microgrids help to increase renewable energy penetration and may be used as basic building blocks for implementing future resilient power grids. However, this integration requires additional layers of communications, both horizontal and vertical, which may substantially increase the potential cyber attack surfaces as well as escalate risks for wide-spread malfunctions of automation systems. The Mulit-layered Resilient Microgrid Networks project is an industry-academic collaboration between ABB, Duke Energy and the University of Illinois at Urbana-Champaign (UIUC) that has developed and demonstrated a cyber-physical resilient control and communication architecture for deployment of multiple microgrid networks, as well as retrofitting the existing distribution grids by increasing DER penetration levels.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Microgrid Handbook for Army Resilience: A Technical Review

The Army recognizes the need for its installations to operate as independently and efficiently as possible with secure energy and water resource that cannot be limited or shut off by external forces or events, whether natural or man-made . In an effort to make this possible, the Army has identified microgrids as an effective way to achieve the goal of secure power for operations, both for domestic and international installations. This document is designed to educate Army Installation leaders about what microgrids are, options for their components, financing, and operations, as well as other regulatory and technological considerations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Simulation and power quality analysis of a Loose-Coupled bipolar DC microgrid in an office building

With distributed generation and battery storage technologies thriving in microgrids, the use of direct current (DC) microgrids in the building sector offers multiple advantages in energy efficiency and power quality compared with alternating current (AC) systems. This study developed a new concept of a loose-coupled bipolar DC building power system. In this work, the concept was used to design a real-world office building in Shenzhen, China. A power system model was developed to study the stability and control of the DC power system and to verify DC power quality. The design and modeling of the DC power control system is discussed in detail. The study developed a few common fault scenarios in DC building microgrids that were simulated in the MATLAB-Simulink environment to validate the design of a loose-coupled bipolar DC system. The results indicate that the proposed loose-coupled bipolar DC system schema, when implemented with proper control algorithms, can achieve good fault-tolerant performance with reliable power quality, even during disruptive system events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Robust trajectory-constrained frequency control for microgrids considering model linearization error

Grid supportive modes integrated within inverter-based resources can improve the frequency response of renewable-rich microgrids. The synthesis of grid supportive modes to guarantee frequency trajectory constraints under a predefined disturbance set is challenging but essential. To tackle this challenge, a numerical optimal control (NOC)-based control synthesis methodology is proposed. Without loss of generality, a wind-diesel fed microgrid is studied, where we aim to design grid supportive functions in the wind turbine. In the control design, linearized models are used, and the linearization-induced errors are quantitatively analyzed by reachability and interval arithmetics and represented in the form of interval uncertainties. Then, the NOC problem can be formulated into a robust mixed-integer linear program. The control structure is strategically configured into two levels to realize online deployment. The proposed control is verified on the modified 33-node microgrid with a full-order three-phase nonlinear model in Simulink. In conclusion, the simulation results show the effectiveness of the proposed control paradigm and the necessity of considering linearization-induced uncertainty.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids

Here, this paper presents a refined quantum Harrow Hassidim Lloyd (HHL) algorithm for microgrid control. The first novelty of the developed method is that a mapping shift function enables the original HHL algorithm to handle general linear equations with non-singular and indefinite matrix. Second, a method of Matrix Extension for Amplifying Sampling Probabilities of Intended Solution (ME-ASPI) is proposed to design the reformulated linear algebraic equations, allowing for improved sampling efficiency of the quantum tomography in the refined HHL algorithm. Then, we applied the method to solve the model predictive control (MPC) problem in nonlinear dynamical microgrids. Specifically, with the ME-ASPI method, the refined HHL algorithm can effectively obtain the intended partial optimal control inputs for MPC. The optimization of quadratic programming problem in each time step of MPC is transformed into a linear system problem, which is addressed by the proposed quantum solver through using only partial information, with the time complexity improved from $\mathscr{O}(\mathscr{N}^{2.37286})$ classically to $\mathscr{O}(\mathscr{N}^{2} log \mathscr{N}$ x $p$ log $p)$ in quantum. Numerical examples have validated the effectiveness of the refined HHL algorithm with the proposed mapping function and the ME-ASPI method. By leveraging quantum properties, the proposed method provides a hybrid quantum–classical framework for microgrid control. This generic method can also potentially tackle many other challenges in analyzing and controlling general complex engineered systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Microgrid design and multi-year dispatch optimization under climate-informed load and renewable resource uncertainty

Microgrids are an increasingly popular solution to provide energy resilience in response to increasing grid dependency and the growing impacts of climate change on grid operations. However, existing microgrid models do not currently consider the uncertain and long-term impacts of climate change when determining a set of design and operational decisions to minimize long-term costs or meet a resilience threshold. In this paper, we develop a novel scenario generation method that accounts for the uncertain effects of (i) climate change on variable renewable energy availability, (ii) extreme heat events on site load, and (iii) population and electrification trends on load growth. Additionally, we develop a two-stage stochastic programming extension of an existing microgrid design and dispatch optimization model to obtain uncertainty-informed and climate-resilient energy system decisions that minimizes long-term costs. Use of sample average approximation to validate our two case studies illustrates that the proposed methodology produces high-quality solutions that add resilience to systems with existing backup generation while reducing expected long-term costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

End-to-end microgrid protection using distributed data-driven methods

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Integration of renewable energy generation and storage systems for emissions reduction in an islanded campus microgrid

Microgrid connected building communities are projected to play an integral part in the clean energy transition. These types of systems, when integrated with distributed energy resources (DERs) such as combined heat and power (CHP), district heating and cooling, renewable generation, and energy storage, can provide clean, reliable power to critical facilities and vulnerable communities. The intermittent nature of renewable generation is a challenge when integrating renewables into any grid system, but particularly in islanded microgrids. The University of Texas at Austin (UT) operates an islanded microgrid powered by a CHP plant, while also utilizing district heating and cooling systems and thermal energy storage (TES). High fidelity operating data was used to develop a validated reduced order model of UT’s integrated campus energy systems to serve as a testbed for use in a case study. Hypothetical renewable energy installations on land owned by UT in west Texas were modeled and integrated into the validated campus models along with battery energy storage (BES). Simulations showed that a combination of renewable energy from wind, and optimally controlled 24-hour thermal and battery storage systems could reduce carbon dioxide emissions on campus by 45.4%. The additional retrofit of burner systems to utilize hydrogen natural gas blends resulted in an overall annual emissions reduction of 54.7%. Carbon capture and storage eliminated the majority of the remaining emissions with increased plant energy expenditure. The presented simulations display the practical limitations of a CHP system complemented by renewable generation and short-term storage in eliminating emissions. Furthermore, results highlight the need for further research and development in long duration storage technologies and hydrogen fueled turbines to increase penetration of renewable energy and reduce emissions.

CHP↗
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The value of integrating a geothermal district heating system into a microgrid

As electrical grids increasingly rely on variable renewable energy, maintaining reliability and cost efficiency becomes more complex. To address these challenges, this study analyzed the integration of geothermal district heating as a grid-responsive thermal resource within a microgrid in Tuttle, Oklahoma. Building energy modeling using EnergyPlus estimated annual district heating demand at 2.9 GWh, with a peak load of 2.8 MW th . Techno-economic analyses were conducted to meet the heating demand under three geothermal scenarios, varying by production depth, flow rate, and thermal output, each supplemented by natural gas peaking boilers. In parallel, equivalent electrical load profiles were developed using typical coefficients of performance (COPs) for air-source heat pumps and electric boilers to establish an electrified baseline scenario. A complete end-use electrical load profile was also developed for the microgrid using Cambium dataset. The modeling results demonstrated reliable and economic operation of the geothermal systems over 30 years, with COPs ranging from 2.6 to 8.9 and the lowest levelized heating cost at $\$$54.6/MWh. Geothermal integration reduced electricity consumption by up to 94.7 % compared to the non-geothermal base case, yielding annual energy savings of up to $\$$803 k. Avoided grid costs ranged from $\$$65 k–$\$$147 k per year, with individual events avoiding up to $\$$4,863 per hour. Grid-responsive operation further reduced wholesale energy costs by 53–56 %. These findings demonstrate geothermal heating, traditionally treated as a non-grid-responsive thermal resource, can be reconfigured to support dynamic grid services, offering a scalable pathway to enhance reliability and reduce costs in renewable-rich microgrids and district heating networks.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

A tool for assessing demand side management and operating strategies for isolated microgrids

Globally, power system operators are exploring ways to leverage the capabilities of smart meters to implement fine-grained demand side management (DSM). Here this paper presents a new simulation tool to evaluate operating strategies for smart-meter-enabled (SME) islanded microgrids being advanced for sustainable rural electrification in emerging economies. Based on MATLAB/Simulink, the tool's component-level models of small microgrids can be easily configured. Alternative rule-based operating strategies are implemented with controllable supply and storage components as well as other components driven by external factors (e.g., weather and time of day). To account for the wide range of possible operating conditions in real-world applications, statistical metrics can be evaluated using Monte Carlo (MC) methods based on user-defined, time-dependent probability distributions for demands, supplies, and environmental variables. Features of the tool are demonstrated with case studies for a representative microgrid in rural Rwanda.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Decentralized Approach for Modeling Organized Convection Based on Thermal Populations on Microgrids

Abstract In this study, a spectral model for convective transport is coupled to a thermal population model on a two‐dimensional horizontal “microgrid,” covering the typical gridbox size of general circulation models. The goal is to explore new ways of representing impacts of spatial organization in cumulus cloud fields. The thermals are considered the smallest building block of convection, with thermal life cycle and movement represented through binomial functions. Thermals interact through two simple rules, reflecting pulsating growth and environmental deformation. Long‐lived thermal clusters thus form on the microgrid, exhibiting scale growth and spacing that represent simple forms of spatial organization and memory. Size distributions of cluster number are diagnosed from the microgrid through an online clustering algorithm, and provided as input to a spectral multiplume eddy‐diffusivity mass flux scheme. This yields a decentralized transport system, in that the thermal clusters acting as independent but interacting nodes that carry information about spatial structure. The main objectives of this study are (a) to seek proof of concept of this approach, and (b) to gain insight into impacts of spatial organization on convective transport. Single‐column model experiments demonstrate satisfactory skill in reproducing two observed cases of continental shallow convection. Metrics expressing self‐organization and spatial organization match well with large‐eddy simulation results. We find that in this coupled system, spatial organization impacts convective transport primarily through the scale break in the size distribution of cluster number. The rooting of saturated plumes in the subcloud mixed layer plays a key role in this process.

54 ENVIRONMENTAL SCIENCES↗