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At least 271 records · Page 15

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DOE OSTI2023

The impact of market design and clean energy incentives on strategic generation investments and resource adequacy in low-carbon electricity markets

Well-designed electricity markets play a crucial role in maintaining reliable electric power systems, which are critical in modern society. Here, this study examines the impact of different electricity market designs and clean energy incentive schemes on supporting renewable energy integration and achieving clean energy goals. To this end, we utilize a game-theoretical generation expansion planning model where generation companies make investment and retirement decisions to maximize their expected profit. The model is structured as an equilibrium problem with equilibrium constraints (EPEC) and solved using a diagonalization approach combined with progressive hedging. We analyze three types of electricity market designs: an energy-only market, a capacity market, and a clean energy market, and consider a wide range of market parameters resulting in 14 total scenarios. Wind and solar capacity comprise the majority of new investments in all considered scenarios, but the resultant system planning reserve margin (PRM) can differ significantly depending on market parameters. We also find that profit-driven investments lead to lower PRMs than a traditional system cost minimization approach. These individual scenario results further demonstrate how different market designs and clean energy incentive schemes may influence investor decision-making and impact resource adequacy throughout the clean energy transition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Multi-parametric analysis for mixed integer linear programming: An application to transmission upgrade and congestion management

Upgrading the capacity of existing transmission lines is essential for meeting the growing energy demands, facilitating the integration of renewable energy, and ensuring the security of the transmission system. This study focuses on the selection of lines whose capacities and by how much should be expanded from the perspective of the Independent System Operators (ISOs) to minimize the total system cost. We employ advanced multi-parametric programming and an enhanced branch-and-bound algorithm to address complex mixed-integer linear programming (MILP) problems, considering multi-period time constraints and physical limitations of generators and transmission lines. To characterize the various decisions in transmission expansion, we model the increased capacity of existing lines as parameters within a specified range. This study first relaxes the binary variables to continuous variables and applies the Lagrange method and Karush-Kuhn-Tucker (KKT) conditions to obtain optimal solutions and identify critical regions associated with active and inactive constraints. Moreover, we extend the traditional branch-and-bound (B&B) method by determining the problem’s upper and lower bounds at each node of the B&B decision tree, helping to manage computational challenges in large-scale MILP problems. Here, we compare the difference between the upper and lower bounds to obtain an approximate optimal solution within the decision-makers’ tolerable error range. In addition, the first derivative of the objective function on the parameters of each line is used to inform the selection of lines for easing congestion and maximizing social welfare. Finally, the capacity upgrades are selected by weighing the reductions in system costs against the expense of upgrading line capacities. The findings are supported by numerical simulations and provide transmission-line planners with decision-making guidance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Electrolyte Lifetime in Aqueous Organic Redox Flow Batteries: A Critical Review

Aqueous organic redox flow batteries (RFBs) could enable widespread integration of renewable energy, but only if costs are sufficiently low. Because the levelized cost of storage for an RFB is a function of electrolyte lifetime, understanding and improving the chemical stability of active reactants in RFBs is a critical research challenge. We review known or hypothesized molecular decomposition mechanisms for all five classes of aqueous redox-active organics and organometallics for which cycling lifetime results have been reported: quinones, viologens, aza-aromatics, iron coordination complexes, and nitroxide radicals. We collect, analyze, and compare capacity fade rates from all aqueous organic electrolytes that have been utilized in the capacity-limiting side of flow or hybrid flow/nonflow cells, noting also their redox potentials and demonstrated concentrations of transferrable electrons. We categorize capacity fade rates as being “high” (>1%/day), “moderate” (0.1–1%/day), “low” (0.02–0.1%/day), and “extremely low” (≤0.02%/day) and discuss the degree to which the fade rates have been linked to decomposition mechanisms. Capacity fade is observed to be time-denominated rather than cycle-denominated, with a temporal rate that can depend on molecular concentrations and electrolyte state of charge through, e.g., bimolecular decomposition mechanisms. We then review measurement methods for capacity fade rate and find that simple galvanostatic charge–discharge cycling is inadequate for assessing capacity fade when fade rates are low or extremely low and recommend refining methods to include potential holds for accurately assessing molecular lifetimes under such circumstances. We consider separately symmetric cell cycling results, the interpretation of which is simplified by the absence of a different counter-electrolyte. We point out the chemistries with low or extremely low established fade rates that also exhibit open circuit potentials of 1.0 V or higher and transferrable electron concentrations of 1.0 M or higher, which are promising performance characteristics for RFB commercialization. We indicate important directions for future research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

A High-Rate Lithium Manganese Oxide-Hydrogen Battery

Rechargeable hydrogen gas batteries show promises for the integration of renewable yet intermittent solar and wind electricity into the grid energy storage. Here, we describe a rechargeable, high-rate, and long-life hydrogen gas battery that exploits a nanostructured lithium manganese oxide cathode and a hydrogen gas anode in an aqueous electrolyte. The proposed lithium manganese oxide-hydrogen battery shows a discharge potential of ~1.3 V, a remarkable rate of 50 C with Coulombic efficiency of ~99.8%, and a robust cycle life. A systematic electrochemical study demonstrates the significance of the electrocatalytic hydrogen gas anode and reveals the charge storage mechanism of the lithium manganese oxide-hydrogen battery. This work provides opportunities for the development of new rechargeable hydrogen batteries for the future grid-scale energy storage.

25 ENERGY STORAGE↗

DOE OSTI2023

Controlling Mass Transport in Direct Carbon Dioxide Zero-Gap Electrolyzers via Cell Compression

The development of high-performance CO 2 electrolyzers is crucial for accelerating the sustainable production of fuels and chemicals integrated with renewable energy sources. Here, we introduce a methodology to actively control mass transport inside a realistic zero-gap membrane electrode assembly of a CO 2 electrolyzer by varying the gasket thickness, which consequently changes the cell compression. This allows control over the thickness and porosity of the gas diffusion electrodes, influencing the overall electrolyzer performance, as demonstrated using Ag-deposited electrodes. At low operating voltages (<2.9 V), both high- and low-compression electrolyzers exhibit similar faradaic efficiencies and partial current densities for CO formation. However, at high voltages, the low-compression electrolyzer with high electrode porosity demonstrates superior CO selectivity and activity with suppressed H 2 formation. These experimental results are validated by the computational membrane electrode assembly (MEA) model developed by using the measured in situ electrode thicknesses and electrode porosities. Additionally, liquid electrolyte saturation at the catalyst layer is found to play a dominant role in determining the mass transport, resulting in a decreased electrolyzer performance with low electrode porosity. Finally, the systematic investigation in this study improves the understanding of the transport dynamics in MEA-based devices and provides insights into optimizing device design parameters for industry-relevant CO 2 electrolysis.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2022

High-energy and low-cost membrane-free chlorine flow battery

Abstract Grid-scale energy storage is essential for reliable electricity transmission and renewable energy integration. Redox flow batteries (RFB) provide affordable and scalable solutions for stationary energy storage. However, most of the current RFB chemistries are based on expensive transition metal ions or synthetic organics. Here, we report a reversible chlorine redox flow battery starting from the electrolysis of aqueous NaCl electrolyte and the as-produced Cl 2 is extracted and stored in the carbon tetrachloride (CCl 4 ) or mineral spirit flow. The immiscibility between the CCl 4 or mineral spirit and NaCl electrolyte enables a membrane-free design with an energy efficiency of >91% at 10 mA/cm 2 and an energy density of 125.7 Wh/L. The chlorine flow battery can meet the stringent price and reliability target for stationary energy storage with the inherently low-cost active materials (~$5/kWh) and the highly reversible Cl 2 /Cl − redox reaction.

25 ENERGY STORAGE↗

DOE OSTI2023

Geospatial mapping of distribution grid with machine learning and publicly-accessible multi-modal data

Abstract Detailed and location-aware distribution grid information is a prerequisite for various power system applications such as renewable energy integration, wildfire risk assessment, and infrastructure planning. However, a generalizable and scalable approach to obtain such information is still lacking. In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road networks, and building maps. Benchmarked against the utility-owned distribution grid map in California, our framework achieves > 80% precision and recall on average in the geospatial mapping of grids. The framework developed with the California data can be transferred to Sub-Saharan Africa and maintain the same level of precision without fine-tuning, demonstrating its generalizability. Furthermore, our framework achieves a R 2 of 0.63 in measuring the fraction of underground power lines at the aggregate level for estimating grid exposure to wildfires. We offer the framework as an open tool for mapping and analyzing distribution grids solely based on publicly-accessible data to support the construction and maintenance of reliable and clean energy systems around the world.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

MFRED, 10 second interval real and reactive power for groups of 390 US apartments of varying size and vintage

Abstract Building electricity is a major component of global energy use and its environmental impacts. Detailed data on residential electricity use have many interrelated research applications, from energy conservation to non-intrusive load monitoring, energy storage, integration of renewables, and electric vs. fossil-based heating. The dataset presented here, Multifamily Residential Electricity Dataset (MFRED), contains the electricity use of 390 apartments, ranging from studios to four-bedroom units. All apartments are located in the Northeastern United States (IECC-climate-zone 4 A), but differ in their heating/cooling system and construction year (early to late 20 th century). To adhere to privacy guidelines, data were averaged across 15 apartments each, based on annual electricity use. MFRED includes real and reactive power, at 10-second resolution, for January to December 2019 (246 million data points). The annual average real power per apartment is 343 W (3.27 W/m 2 of floor area), with strong variation between seasons and apartment size. Considering its large number of apartments, high time resolution, real and reactive power, and 12-month duration, MFRED is currently unique for the multifamily-sector.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Electrochemical lithium extraction from hectorite ore

Electrochemical technologies add a unique dimension for ore refinement, representing tunable methods that can integrate with renewable energy sources and existing downstream process flows. However, the development of electrochemical extraction technologies has been impeded by the technological maturity of hydro- and pyro-metallurgy, as well as the electrical insulating properties of many metal oxide ores. The fabrication and use of carbon/insulating material composite electrodes has been a longstanding method to enable electrochemical activation. Here, using real hectorite ore, we employ this technical approach to fabricate hectorite-carbon black composite electrodes (HCCEs) and achieve electrochemical activation of hectorite. Anodic polarization results in lithium-ion release through a multi-step chemical and electrochemical mechanism that results in 50.7 ± 4.4% removal of lithium from HCCE, alongside other alkaline ions. This technical proof-of-concept study underscores that electrochemical activation of ores can facilitate lattice deterioration and ion removal from ores.

25 ENERGY STORAGE↗

DOE OSTI2020

Chapter 8. Hybrid Biological–Inorganic Systems for CO 2 Conversion to Fuels

The biological reduction of CO 2 into organic carbon is integral to the formation and maintenance of all life on earth. An urgent need to mitigate the effects of CO 2 on our atmosphere has provided an imperative to exploit biological CO 2 fixation for the conversion of CO2 into valuable and energy rich fuels. Photosynthesis uses only sunlight, water, and CO2 as starting materials to form all biomass.

CO2 Conversion to Fuels↗

DOE OSTI2025

Analytical Identification Method of Generalized Short‐Circuit Ratio Using Phasor Measurement Units

This paper introduces a novel analytical approach for the identification of the admittance matrix and the generalized short-circuit ratio (gSCR) in power systems integrated with renewable energy sources. The proposed method leverages voltage and current measurements from phasor measurement units (PMUs) to construct a least squares objective function, which is then solved using matrix calculus and partial derivatives. Unlike conventional optimization algorithms, this approach provides an analytical solution that substantially reduces data requirements, enabling the efficient and accurate identification of the gSCR with smaller datasets. Additionally, its fixed computational complexity allows for real-time updates as new data are collected, ensuring continuous refinement of the system of equations and enabling rapid, precise gSCR calculations. The method also exhibits strong robustness against measurement noise, making it well-suited for practical applications in dynamic power systems. The combination of reduced data requirements, real-time adaptability, noise robustness and fixed computational load establishes this method as a highly efficient and reliable tool for real-time power system stability analysis. Case studies on an EPRI 36-bus system demonstrate the method's effectiveness, highlighting its accuracy in closely matching true gSCR values, even under diverse disturbances and noisy conditions.

Han, Zelei [Hohai University, Nanjing (China)] (OR↗

DOE OSTI2025

Gaussian FLOWERS: Wind-rose-based analytical integration of Gaussian wake model for extremely fast AEP estimation

A major cost in the study of wind farm layout optimization is the repeated evaluation of the annual energy production (AEP). The current approach to estimating AEP requires a large set of flow simulations to be performed that cover each discrete wind speed and direction combination contained within the wind rose, followed by a probability-weighted sum of the power production resulting from each simulation. Even with inexpensive engineering wake models, this numerical integration scheme can lead to high computational costs. In this paper, we derive an analytical formulation for estimating farm AEP across every wind direction, based on a Gaussian wake velocity model, which reduces the number of wind farm simulations to a single function evaluation. As a result, we find that the Gaussian-FLOWERS approach reduces the time for AEP calculations by more than two orders of magnitude with a small trade-off in accuracy when compared to a conventional approach. This massive reduction in computation cost is useful to reduce overall costs in wind farm layout optimization studies.

17 WIND ENERGY↗

DOE OSTI2024

Designing Ta C Virtual Substrates for Vertical Al x Ga 1 − x N Power Electronics Devices

Power electronics are critical for a sustainable energy future, playing a key role in electrification and integration of renewable energy sources into the grid. Advances in ultrawide band gap materials are needed to handle higher powers in smaller form factors while reducing electrical and thermal losses. High Al content Al x Ga 1 − x N is theoretically capable of meeting these demands, but its impact in power electronics has been severely restricted by a lack of substrates that can satisfy conductivity, lattice matching, and/or thermal expansion requirements. We demonstrate that electrically conductive Ta C can be used as a virtual substrate for Al x Ga 1 − x N heteroepitaxy. Scaleably sputtered Ta C grown on Al 2 O 3 , followed by high-temperature face-to-face annealing, produces a thin film Ta C template with an effective hexagonal lattice constant matched to Al 0.70 Ga 0.30 N . Annealing of the Ta C promotes recrystallization, significantly improving crystallinity and reducing crystalline defects from as-deposited columnar grains to a step-and-terrace surface morphology, enabling the subsequent growth of high-quality Al 0.70 Ga 0.30 N by molecular beam epitaxy. X-ray diffraction and scanning transmission electron microscopy confirm that the Al x Ga 1 − x N layer is heteroepitaxially aligned, strain-free, and lattice-matched, transitioning abruptly from Ta C to Al x Ga 1 − x N without intermediate phases. These results demonstrate Ta C virtual substrates as electrically conductive, lattice-matched, and thermally compatible templates for vertical Al x Ga 1 − x N devices that can meet the growing power needs of a sustainable energy future. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗

DOE OSTI2020

Vulnerability Detection Methodology for a Digital Signal Processor Micro-Controller at Edge Level Distributed Energy Resource Controller

Integration of renewable energy and energy storage based Distributed Energy Resource (DER) assets to the grid has seen an upsurge over the past few decades. Traditionally, energy would be generated at a thermal or nuclear power plant and then transmitted over long distance through the transmission grid and then supply to the customers at the distribution grid. Modern renewable energy based DER assets have brought energy production at the edge of distribution grid, which has made the grid vulnerable to cyber intrusion as the power flow controllers for such DER assets are now distributed across the grid from distribution level to transmission level. This paper discusses the vulnerability posed by such power flow controllers of DER assets and demonstrates a detection methodology for unauthorized access and manipulation of system configuration for Digital Signal Processor based Microcontrollers. Experimental results proving efficacy of the methodology have been shown in paper.

Bhowmik, Pankaj↗

DOE OSTI2022

Design and Implementation of Automated Characterization of T-type based Power Module for PV Inverter Reliability Assessment

Reliability of power electronics holds the key for future power and energy systems since it is essential for system integration with renewables and energy storage systems. Variation of power module characteristics over the converter’s operational lifetime is a critical indicator to assess its reliability. Device characterization consists of different steps to collect, process, and visualize results, which is usually time-consuming, especially for power modules based on a non-phase-leg configuration, such as T-type circuits, widely applied for PV inverters. To reduce the time, ensure repeatability, and guarantee consistency, an automated test bench can be helpful. This paper focuses on a T-type-based power module and proposes a unique characterization platform and corresponding automation procedure and implementation. Considering the major characterization difference between T-type power modules over the conventional phase-leg power module, first, the special consideration for static characterization is highlighted. Second, the unique dynamic characterization design associated with the T-type power module is described. Finally, the automation of the power module characterization and the test platform is presented along with results and special considerations.

Siraj, Ahmed↗

DOE OSTI2025

Optimizing Grid Regulation With Gravity Storage Systems: A Comparative Analysis With Different Motor Inertias

The integration of renewable energy sources into power grids necessitates solutions for grid support and stability during fluctuations in electricity generation and demand. Gravity energy storage systems (GESS) are emerging as a promising technology for managing the balance between energy supply and demand. However, their capacity to optimize energy flow and offer voltage and frequency regulation amid imbalances in generation and demand is less reported. This paper investigates the control of GESS for optimizing energy flow during voltage and frequency regulation. The study evaluates the regulation capabilities of GESS with different motor inertias during a Texas grid event: one with a high-speed, low-inertia motor and another with a low-speed, high-inertia motor. Results indicate that both GESS scenarios provide fast frequency response by converting potential energy into kinetic energy and vice versa, with a response time of 1.5 s from the frequency variation. This aligns with grid requirements for primary frequency response from traditional synchronous generators and motors with large inertia. Furthermore, a GESS based on a high-inertia motor may be able to operate over a broader range of frequency variations, whereas a low-inertia system may be limited by thermal constraints of the motor.

frequency regulation↗

DOE OSTI2023

Modeling of a Networked Community Microgrid with High Solar Penetration Considering Control Dynamics of Inverter-Based Resources

Microgrids help facilitate the integration of renewable energy in distribution-level grids and increase the resilience of the electric grid to extreme weather, especially in rural areas. Compared to traditional microgrids, a networked microgrid leverages multiple grid-forming sources to form a potential meshed grid and is more flexible in operation. This paper demonstrates the simulation modeling of an actual networked microgrid located in Adjuntas, Puerto Rico. The model contains representations of power inverters that connect the battery energy storage systems and photovoltaic generation systems to the networked microgrid and is capable of simulating fast grid transients as well as long-term operation of the networked microgrid. The modeling technique for power inverters allows the time-efficient simulation of the microgrid with a minimal penalty on model accuracy.

Li, Dingrui↗

DOE OSTI2020

DeepGrid: Robust Deep Reinforcement Learning-based Contingency Management

Increasing uncertainty raised by the integration of renewable energy resources requires an enormous number of simulations to be carried out for the security assessment of the power grid. However, it is challenging to assess the steady-state and dynamic security indices for different system contingency events by doing an exhaustive analysis in real-time due to the computational and communication constraints. One promising solution is using data-driven techniques along with the system models to train an intelligent contingency management framework to better handle the contingencies in real-time. Nevertheless, implementing a data-driven technique to obtain the best remedial actions necessitates to account for the effect of the measurement noise on the performance of the contingency management. To tackle these challenges, we leverage a robust deep reinforcement learning (DRL) algorithm called Double Deep Q-Network (DDQN) to design a recommender system capable of prescribing optimal control actions with the help of the real-time digital simulator (RTDS). The use of RTDS system in combination with the advanced DRL algorithm allows to explore a wide variety of system contingencies in order to derive better remedial actions. The performance of the proposed algorithm is evaluated in IEEE 9-bus system under different loading conditions, and different network configurations in presence of noisy measurements.

Ghasemkhani, Amir↗