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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 OSTI2026

Benchmarking of three DWM-based wake models at below-rated wind speeds

Wind turbine wake models are essential tools for predicting power losses and structural loads in wind farms. Among these, the dynamic wake meandering (DWM) model, included as a recommended approach in the International Electrotechnical Commission design standard, is a widely used engineering-fidelity method that balances accuracy and computational cost. This study compares the performance of three DWM-based wake model implementations (from the Technical University of Denmark, the National Renewable Energy Laboratory, and the Institute for Energy Technology) under below-rated wind speed conditions. Model predictions of wake flow, power output, and structural loads for a four-turbine row are evaluated across different ambient turbulence levels and wind-direction misalignments and compared against high-fidelity large-eddy simulation results. All three models captured the overall wake evolution and mean turbine performance with reasonable accuracy; their predicted time-averaged thrust and power were typically within 5 %–10 % of the large-eddy simulation benchmark. However, notable differences emerged in wake structure and unsteady load predictions, with discrepancies increasing for turbines further downstream. These differences highlight the importance of modelling choices such as wake summation and turbulence treatment, which strongly influence power-deficit and fatigue-load predictions. Comparison with large-eddy simulations reveals each approach's strengths and weaknesses, indicating where improvements are needed. Overall, the findings point to specific refinements for DWM models to improve their fidelity, ultimately enabling more robust wake predictions for wind farm design and operation.

17 WIND ENERGY↗
DOE OSTI2021

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Synthetically generated, large graph networks serve as useful proxies to real-world networks for many graph-based applications. The ability to generate such networks helps overcome several limitations of real-world networks regarding their number, availability, and access. Here, we present the design, implementation, and performance study of a novel network generator that can produce very large graph networks conforming to any desired degree distribution. The generator is designed and implemented for efficient execution on modern graphics processing units (GPUs). Given an array of desired vertex degrees and number of vertices for each desired degree, our algorithm generates the edges of a random graph that satisfies the input degree distribution. Multiple runtime variants are implemented and tested: 1) a uniform static work assignment using a fixed thread launch scheme, 2) a load-balanced static work assignment also with fixed thread launch but with cost-aware task-to-thread mapping, and 3) a dynamic scheme with multiple GPU kernels asynchronously launched from the CPU. The generation is tested on a range of popular networks such as Twitter and Facebook, representing different scales and skews in degree distributions. Results show that, using our algorithm on a single modern GPU (NVIDIA Volta V100), it is possible to generate large-scale graph networks at rates exceeding 50 billion edges per second for a 69 billion-edge network. GPU profiling confirms high utilization and low branching divergence of our implementation from small to large network sizes. For networks with scattered distributions, we provide a coarsening method that further increases the GPU-based generation speed by up to a factor of 4 on tested input networks with over 45 billion edges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Toward 100% Renewable Power Grids: A Review

The transition to a 100% renewable power grid remains beset by significant technical challenges. This paper critically examines the obstacles arising from the variability and unpredictability of renewable energy sources (RES), which complicate the real-time balancing of power generation and load demand. In addition to traditional stability concerns (e.g., rotor angle, voltage, and frequency stability), the increasing penetration of inverter-based resources (IBRs) introduces novel challenges, including resonance stability issues and converter-driven dynamics. Moreover, the dynamic behavior and fault response of IBRs diverge markedly from those of conventional synchronous generators, rendering traditional protection schemes increasingly inadequate. This paper reviews state-of-the-art solutions in power balancing, grid flexibility, stability enhancement, and advanced protection strategies, discussing their implications for future grid design. The analysis provides a comprehensive assessment of recent technological advancements, thereby outlining critical research directions essential for achieving a resilient, 100% renewable power grid.

100% renewable power grid↗
DOE OSTI2022

A Self-Organizing Nano Grid (SONG) for Energy Access Clusters

In this paper, a bottom-up dc Self-Organizing Nano Grid (SONG) for rural electrification at ultra-low-cost through a flexible and scalable topology is presented. The proposed nano grid requires no communication and uses dc-bus signaling to convey excess energy in the system through the bus voltage, allowing independent agents to conduct energy transactions based on their needs in a decentralized manner. Moreover, this article proposes a concept that uses a novel multi-source, multi-segment droop control strategy with demand coordination, allowing for dynamic self-balancing of the system under fluctuating operating conditions. The unique use of a surplus photovoltaic (PV) power droop mode precisely estimates energy availability to minimize load chattering that helps in stabilizing the dc-bus. In addition, this architecture uses standard dc/dc converters for a low-cost, modular, Plug and Play ( PnP ) solution with highly automated operation, enabling local assembly, operation, and maintenance by non-technical personnel. The proposed SONG concept was validated in simulation and with a 700W, laboratory-scale, custom made nano grid system. The simulation and experimental test cases show the feasibility of the SONG system by demonstrating the proposed multi-source, multi-segment droop control strategy without load chattering under varying operating conditions.

dc-dc converter↗
DOE OSTI2023

Unlocking the price

To help balance load and power generation that ensue from the electrification of transportation and the increased connection of variable power sources to the grid, ABB has developed an RL-based dynamic price model for EV charging with the required flexibility to respond to changing grid conditions.

Suryanarayana, Harish↗
DOE OSTI2025

A Scalable Hardware-and-Human-in-the-Loop Grid-interactive Efficient Building Equipment Performance Dataset

This project developed a publicly available, high-fidelity dataset about the interactions among humans, homes, and heat pumps supporting grid interactive efficient buildings to balance demand on the grid with comfort for occupants. Laboratory measurements and simulations of the hardware capture the second-scale electric power dynamics of heat pumps providing grid services like load shifting and load shedding. Field measurements, behavior tracking, and qualitative surveys of people in their homes over multiple years—including experimentally adjusting the heating and cooling system to provide grid services—to capture the reciprocal effect of human behavior on grid services, and grid services on human comfort. Taken together, these data capture the complete Hardware and Human in the loop system for residential heat pumps, reducing large uncertainties in simulation for design, and models for control of heat pumps, and grid-interactive buildings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

CoREC: Scalable and Resilient In-memory Data Staging for In-situ Workflows

The dramatic increase in the scale of current and planned high-end HPC systems is leading new challenges, such as the growing costs of data movement and IO, and the reduced mean time between failures (MTBF) of system components. In-situ workflows, i.e., executing the entire application workflows on the HPC system, have emerged as an attractive approach to address data-related challenges by moving computations closer to the data, and staging-based frameworks have been effectively used to support in-situ workflows at scale. However, the resilience of these staging-based solutions has not been addressed, and they remain susceptible to expensive data failures. Furthermore, naive use of data resilience techniques such as n-way replication and erasure codes can impact latency and/or result in significant storage overheads. In this article, we present CoREC, a scalable and resilient in-memory data staging runtime for large-scale in-situ workflows. CoREC uses a novel hybrid approach that combines dynamic replication with erasure coding based on data access patterns. It also leverages multiple levels of replications and erasure coding to support diverse data resiliency requirements. Furthermore, the article presents optimizations for load balancing and conflict-avoiding encoding, and a low overhead, lazy data recovery scheme. We have implemented the CoREC runtime and have deployed with the DataSpaces staging service on leadership class computing machines and present an experimental evaluation in the article. Here, the experiments demonstrate that CoREC can tolerate in-memory data failures while maintaining low latency and sustaining high overall storage efficiency at large scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Further, negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

The Hydropower Game: An Interactive Learning Tool for the Future Hydropower Workforce

This report documents a Seedling project supported by the U.S. Department of Energy (DOE) Water Power Technologies Office (WPTO), conducted by Argonne National Laboratory (ANL) and the National Laboratory of the Rockies (NLR). The Seedling and Sapling Program provides small grants over short duration to enable early-stage research at national laboratories. This funding mechanism is intended to cultivate innovative ideas and expand research road maps in hydropower and marine energy technologies. Under this Seedling award, the project team developed an educational outreach tool, or “serious game”, built in Python and powered by the Pygame library, aimed at teaching fundamental hydropower concepts through an engaging, interactive learning experience. The game features multiple lessons covering several hydropower topics while allowing for expansion and customization in the instance of future funding availability: • Hydropower Plant Types: Players explore and compare the mechanics and applications of run-of-river, dam hydropower, and pumped-storage hydropower. • Flow Dynamics and Power Generation: Interactive tasks demonstrate how release rate and hydraulic head combine to determine power output. • Grid Operations and Load Following: Simulations illustrate how hydropower facilities respond to fluctuating electricity demand to balance the grid. • Market Integration: Levels scaffold understanding of how hydropower interfaces with the broader energy market, including operational and economic considerations. The tool was showcased at several science, technology, engineering, and mathematics (STEM) outreach events, where it was demoed to students, educators, and the general public. These events provided valuable opportunities to collect feedback on the gameplay, storyline, and educational objectives from a wide audience. The game was presented at STEMapalooza, Introduce a Girl to Engineering Day, STEMCON, and Clean Currents 2025. Insights gathered during these demonstrations informed refinements to the game’s user experience and strengthened its effectiveness as an educational tool for teaching hydropower concepts. This report outlines the game’s design philosophy, educational objectives, technical implementation, user experience insights, and potential for broader deployment within educational and workforce development contexts. It emphasizes how gamified learning can demystify complex hydropower science and inspire interest in water-power technologies. The project’s success demonstrates the value of Seedling funding in fostering creative, lowcost educational tools that support DOE’s mission to advance energy literacy and innovation. The report concludes with recommendations for expanding the tool by adding new levels, integrating assessment metrics, and exploring commercialization or deployment pathways through future Sapling funding. The official webpage of the Hydropower Game, which includes a link to the educational tool, can be accessed at www.anl.gov/hydropower/hydropower-game.

13 HYDRO ENERGY↗
DOE OSTI2022

Cyber-Physical Dynamic System (CPDS) Modeling for Frequency Regulation and AGC Services of Distributed Energy Resources

The substantial integration of renewable energy brings significant challenges to balance the system in real time because of the variability and intermittency of renewable power. For the reliable system operation, the frequency regulation service is used to stabilize the system frequency through automatically balancing the system generation and load. On one hand, with the substantially increasing deployment of renewable energy in electricity system, the requirement of frequency regulation (FR) services increases significantly. On the other hand, the current main resource of FR services, the controllable conventional generation, is continuously decreasing in the system generation mix. This means that in the future high renewable penetration power system, additional and alternative reliable FR services providers such as distributed energy storage (DES) resources should be explored. Although the capability of utility-scale energy storage to provide FR services has been demonstrated, the integrated control and dynamic modeling of distributed energy resource (DER) providing frequency regulation grid services has been rarely explored. There are several challenges to adopt DERs to provide reliable grid services as illustrated in FERC 755. First, the distributed installation of DES requires a comprehensive cyber-physical dynamic system (CPDS) modeling to fully consider the impacts of the communication latency variability on its real time FR provision capability. Unlike the conventional generators, there are two-layer communication between DERs and system operators which increase the communication delay. Second, unlike the conventional generators whose dynamic models have been comprehensively studied, the difference among individual DER components' power-dynamic characteristic brings challenges in the accurate dynamic AGC modeling of its power-frequency relationship. Third, the temporal dependent state of charge uncertainty of DERs challenges DERs' power and frequency regulation capacities scheduling in the look-ahead generation scheduling. Therefore, the aggregator should optimize the frequency regulation provision from individual DER in real time. To overcome these challenges, this project proposes a cyber-physical dynamic system (CPDS) model to handle the uncertainty of DERs two-layer communication latency and power dynamics. The variability of DERs' communication delay and dynamic constraints will be comprehensively modeled. The DERs' AGC model with communication delay is designed to validate DESs' frequency regulation services. Like current performance-based frequency regulation evaluation, the delivery of DERs' frequency regulation will be assessed through post-analysis of the actual AGC response with respect to the AGC control signal from system operators. Consequently, the reliability improvement with DERs providing reliable frequency regulation services can be evaluated from a comprehensive perspective considering all the dynamics of communication and power.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Synchronous Machine Governor Upgrade

Conventional generation sources play a critical role in the stability and reliability of the electrical grid, particularly as we transition towards more renewable energy sources. To understand and accurately emulate their behavior for optimizing grid operations and ensuring seamless integration with renewable technologies, it is essential to better emulate the grid- and plant-level impacts of conventional generation sources, such as natural gas (NG) driven heat recovery steam generators (HRSGs) and combustion turbines (CTs). Therefore, a governor model is developed in a programmable logic controller (PLC) to investigate the performance of the conventional generator under various dynamic operating conditions and to identify the impact on grid stability in a controlled environment. The governor model aims to enable the hardware-in-the-loop (HIL) based emulation of these conventional generation sources using the existing 2 MVA synchronous machine/generator that is driven by a flexible 2.5 MW variable speed drive. This setup will allow us to replicate the dynamic characteristics and response behaviors of NG-driven HRSGs and CTs. The controls for the emulated conventional plants follow the industry standard and are adjustable, ensuring they accurately reflect the operational capabilities and limitations of real-world systems. These controls include load-following capabilities, ramp rates, startup and shutdown sequences, and emissions characteristics. By incorporating these adjustable controls, we aim to capture the nuanced impacts of conventional generation, such as their ability to provide ancillary services like frequency regulation, voltage support, and spinning reserve. In this report, we simulate two types of dynamic operations: grid-connected and islanding. For each dynamic operation, representative starting sequences are tested, including turbine purge, ignition, speed ramping up, generator excitation and synchronizing, and breaker close. The HIL based tests provides insights for field deployment, specifically the high-fidelity governor model provides results to predict the potential stability and reliability risk and suggest possible integration measures (e.g., generation and load balancing, tuning of governor control parameters). Ultimately, this enhanced emulation capability will be integrated into our Advanced Research on Integrated Energy Systems (ARIES), enabling us to conduct comprehensive studies on the interactions between conventional and renewable energy sources. By better understanding these interactions, we can develop strategies to optimize the overall performance and reliability of the grid. This will support the deployment of advanced grid management techniques, such as demand response, grid-forming inverters, and energy storage systems. The main contributions are summarized as follows: (1) This report introduces a PLC-based governor model for gas turbines. This model accurately simulates the dynamic behavior of conventional generation sources under various operational scenarios; (2) The model is integrated with an HIL testbed that includes a 2.5 MW variable speed drive and a 2 MVA synchronous machine. This setup enables realistic, real-time emulation of conventional power plants, particularly NG driven HRSGs and CTs; (3) The developed model is adaptable to various gas turbine configurations and allows for precise control over parameters such as MW ramp rates. This flexibility makes it a valuable tool for future research and industry collaboration; and (4) By incorporating the model into the National Renewable Energy Laboratory's Advanced Research on Integrated Energy Systems, the report lays the groundwork for future studies on interactions between conventional and renewable energy sources, enhancing the ability to develop advanced grid management strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Defining a Platform Approach and Market Participation: Data Driven Business Models for Solid State Transformer-Based Synthetic Inertia and Voltage Stability Controls (CRADA Final Report, Project 1, Mod 1)

The primary objective of this project is to determine the incremental value created with the medium voltage solid-state transformer (MV SST) technology to different stakeholders in view of the updated DER grid regulations. This includes studying the benefits of the MV SST technology in a range of use cases for EV and DER penetration including (1) “corridor charging” for EVs and (2) solar plus storage (FERC 2222). The potential customers of this technology include utilities for EV charging, DER installers who must meet utility interconnection requirements, balancing authorities, and DER aggregators. The traditional transformers on the grid could be a limiting factor for the EV-grid integration as the distribution transformers were not designed to handle the dynamic and fluctuating EV charging loads. Thus, the issues such as voltage fluctuations, increased losses and reduced efficiency [1] can negatively impact the grid operation. To address these challenges, transformers with flexibility and adaptability become imperative to meet the evolving energy demands. In this regard, the concept of Medium Voltage Solid-State Transformers.

14 SOLAR ENERGY↗
DOE OSTI2025

A Hybrid Fuel Cell and Battery Storage Power Management for Grid-Interactive EV Charging Station

With the increasing adoption of renewable energy sources in grid-interactive Electric Vehicle (EV) charging stations, the role of energy storage systems has become critical. While large energy storage systems have mitigated the intermittency of renewable energy, integrating multi-source energy management with prioritized charging can further enhance the reliability of charging stations (CS). This paper presents a decentralized energy management (DEM) approach combining battery energy storage (BES) and fuel cell (FC) systems using a rule-based line resistance correction droop (LRCD) control technique. The proposed droop control dynamically adjusts the gain to balance the state-of-charge (SoC) of the BES, enhancing power support longevity and improving battery life under varying capacity conditions by reducing current stress. Additionally, the paper addresses the challenges of using fuel cells in linear regions to optimize efficiency and manage various charging scenarios. The CS integrates unity power factor grid interaction, and power support for auxiliary loads, maintaining harmonic distortion within 5% during grid islanding. The approach evaluates DC bus voltage regulation under various scenarios of PV array power fluctuations and dynamic load variations, in both grid-connected and standalone operations. In conclusion, the proposed control strategy is validated on a laboratory prototype through various dynamic load variation and grid islanding scenarios.

Khalid, Mohd [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2025

Coupled momentum balance and phase-field solver with fenicsx module

Code solves momentum balance and phase-field equations simultaneously. The differential equations are solved on a discretized domain with appropriate boundary and initial conditions using finite element method. Primary purpose of the code is to simulate brittle fracture under dynamic loading. Constitutive equations are that of linear elasticity with degradation of stress due to fracture. Small strain formulation is used.

Zecevic, Milovan↗
DOE OSTI2022

Performance Analysis of Speculative Parallel Adaptive Local Timestepping for Conservation Laws

Stable simulation of conservation laws, such as those used to model fluid dynamics and plasma physics applications, requires the satisfaction of the so-called Courant-Friedrichs-Lewy condition. By allowing regions of the mesh to advance with different timesteps that locally satisfy this stability constraint, significant work reduction can be attained when compared to a time integration scheme using a single timestep size. However, parallelizing this algorithm presents considerable difficulty. Since the stability condition depends on the state of the system, dependencies become dynamic and potentially non-local. In this article, we present an adaptive local timestepping algorithm using an optimistic (Timewarp-based) parallel discrete event simulation. We introduce waiting heuristics to limit misspeculation and a semi-static load balancing scheme to eliminate load imbalance as parts of the mesh require finer or coarser timesteps. Last, we outline an interface for separating the physics of the specific conservation law from the temporal integration allowing for productive adoption of our proposed algorithm. We present a misspeculation study for three conservation laws, demonstrating both the productivity of the local timestepping API, for which 74% of the lines of code are reused across different conservation laws, and the robustness of the waiting heuristics—at most 1.5% of element updates are rolled back. Our performance studies demonstrate up to a 2.8× speedup versus a baseline unoptimized local timestepping approach, a 4x improvement in per-node throughput compared to an MPI parallelization of synchronous timestepping, and scalability up to 3,072 cores on NERSC’s Cori Haswell partition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Energy Arbitrage: Comparison of Options for use with LWR Nuclear Power Plants

Arbitrage is the opportunistic buying and selling of a commodity during local pricing valleys and peaks respectively to maximize economic value. This report evaluates options for energy arbitrage integrated with existing light water reactor (LWR) nuclear power plants (NPPs) where nuclear energy could be stored in a variety of forms and later recovered to generate electrical power during periods when grid electricity demand and pricing are high. The forms of energy storage examined in this report include the potential value of batteries, hydrogen, and thermal energy storage for coupling with nuclear power. Various large demand response options are also analyzed, including the production of liquid nitrogen via air separation and liquefaction, liquefaction of hydrogen, compressed hydrogen, and the cryogenic capture of CO2. Demand response refers to dispatchable loads that can cycle up or down depending on-grid electricity demand to aid in balancing the grid. Large demand response options could dispatch to aid nuclear power stations in avoiding power turndowns by providing an alternate disposition for electrical energy by producing marketable products (e.g., liquid nitrogen, hydrogen, or captured CO2). Static conditions were chosen and analyzed in this report for each option. Dynamic operation or optimization of energy arbitrage or demand response are out of scope for this report. The analysis is based on storage systems with discharge capacities of 500 MW for which various durations of storage and costs of charging (electricity cost) are examined. While the value of thermal energy to an industrial user for flexible plant operations has been previously proven as a business case, this report evaluates costs of hydrogen energy storage and leading thermal energy storage options, and large demand response loads that could be integrated with LWRs in comparison to utility-scale battery storage for use of off-peak nuclear energy. Compilation of this information will be used by the Idaho National Laboratory (INL) RAVEN/HERON systems integration and economics tool to evaluate thermal energy dispatch to industrial users. Relative ranking of energy storage options was done using a levelized cost of storage (LCOS) metric which calculates a rough breakeven cost for the system, taking into account the capital and operating costs as well as the revenue from arbitrage. Table ES1 below shows the LCOS for each of the energy storage options considered. First, in the table, lithium iron (Fe) phosphate batteries are listed as the base case for comparison against the other options. Next is hydrogen storage where most of the hydrogen analyses assumed the hydrogen to be produced using solid oxide electrolytic cell (SOEC) high temperature steam electrolysis (HTSE). The others used existing models of polymer electrolyte membrane (PEM) low temperature electrolysis to produce hydrogen. HTSE performance parameters and costs were taken from existing INL models. Various means were assumed to convert the hydrogen to electricity, including PEM fuel cells (FCs) and a gas turbine mixed in a 30 vol% mixture with natural gas. Physical storage (pressure vessels) and geological storage (natural underground features) were used to store the hydrogen as noted. Geological storage is more economical, but the locations are limited because of the requirement for pre-existing geological formations that will support storage. Thermal energy storage (TES) options were also analyzed including electro-thermal energy storage (ETES) and four different liquid sensible heat TES storage media as noted (Hitec, Hitec XL, Therminol-66, and Dowtherm A). The ETES process considered was modified using existing public documentation on an Echogen process and uses a separate supercritical CO2 charge and discharge cycle with sand as the heat storage media.

25 ENERGY STORAGE↗
DOE OSTI2022

A Novel Grid-forming Voltage Control Strategy for Supplying Unbalanced Microgrid Loads Using Inverter-based Resources

This paper presents a novel grid-forming voltage control strategy for a battery energy storage system to maintain balanced three-phase output voltages when serving unbalanced loads. A stationary reference frame (αβ) based control scheme is proposed to regulate positive-sequence and negative-sequence voltages. Compared with the conventional rotating reference frame (dq) based control scheme, the proposed scheme shows better dynamic performance. Then, we analyze the system zero-sequence network and propose adding a grounding transformer to the Y/Yg connected output transformer to further reduce the voltage unbalance at the point of common coupling. The simulation results demonstrate the effective performance of the proposed voltage control scheme. Based on the results, the power-voltage unbalance curve is derived for different output transformer configurations to establish the relationship between the power unbalance limit and the voltage unbalance limit for microgrid power scheduling.

Xu, Bei↗
DOE OSTI2022

A data-driven linear formulation of the optimal demand response scheduling problem for an industrial air separation unit

Demand response (DR) has become a key element in balancing the power grid as the contribution of time-varying renewable power generation increases. Chemical plants are appealing candidates for DR programs as they offer large, concentrated and flexible loads. DR participation calls for frequent production rate changes over time scales that overlap with the dominant dynamics of the plant. Production scheduling should therefore consider the process dynamics explicitly. Here we present a data-driven approach for modelling the scheduling-relevant dynamics based on historical closed-loop operating data using autoregressive with extra inputs (ARX) models. We introduce a new, linear scheduling problem formulation based on the ARX representation, and demonstrate its implementation on an industrial air separation unit.

42 ENGINEERING↗