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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deep Learning Based Frequency Stability Assessment in Power Grid with High Renewables

Frequency stability assessment is one critical aspect of power system security assessment. Traditional N-1 screening method is based on the simulations of a few typical daily and seasonal operation scenarios. However, the increasing integration of inverter-based renewables and the retirement of conventional synchronous generators result in decreasing system inertia and growing complexity of system operating conditions. Selecting a few typical operation scenarios cannot cover all operating conditions, and the time-domain simulation of all operation conditions requires tremendous time. This paper proposes a more efficient frequency stability assessment method based on deep learning. The affinity propagation clustering algorithm is used to divide the dataset into different clusters, so the selected dataset for training can cover the diversified operating conditions as much as possible. Also, feature normalization is applied to both the training dataset and testing dataset in order to remove any unnecessary bias. Especially, trained model based on full dataset normalization has bounded error in the prediction. The case study on the reduced 240-bus WECC system demonstrates that the proposed method can predict accurate frequency nadir with limited training dataset. The deep learning model using the revised feature normalization can predict more accurate frequency nadir than that using the traditional feature normalization and has very small maximum prediction error.

affinity propagation↗
DOE OSTI2022

Generating Sequential PV Deployment Scenarios for High Renewable Distribution Grid Planning

This paper introduces a novel approach for generating solar photovoltaic (PV) plant deployment scenarios for grid integration planning. The approach guarantees consistency among scenarios of the same deployment by ensuring that higher penetration scenarios contain PV units deployed in lower penetration scenarios. It also constrains the size and spatial distribution of the PV plants and considers three placement types. A case study on a real-world distribution system proves that the precepts of scenario consistency, deployment diversity, and placement are met. The study further investigates the impact of the resulting scenarios via a stochastic hosting capacity analysis. Results indicate that the ratio between PV and load sizes, referred to as the nodal PV penetration factor (NPPF), is a key driver of the grid integration impact. By reducing the NPPF from 5 to 2, the maximum hosting capacity increased by at least 112%. The study also reveals that scenarios under random placement can lead to higher hosting capacity values.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2020

Probabilistic Assessment of High-Renewables Power Systems: Current Work and Future Directions

In a future with higher penetrations of variable and energy-limited resources, the dominant sources of resource adequacy risk begin to shift from (assumed) independent thermal unit outages to spatially- and temporally-correlated weather-driven phenomena. Traditional resource adequacy assessment and planning methods can be ill-suited to such scenarios. This presentation will highlight aspects of NREL's work in adapting and developing new probabilistic methods and tools in anticipation of the needs of the power system planning exercises of tomorrow.

17 WIND ENERGY↗
DOE OSTI2022

Modeling Distributed Energy Resources for Analyzing Distribution Systems with High Renewable Penetration: Preprint

Increasing levels of inverter-based distributed energy resources (IBDERs) impact the legacy overcurrent distribution protection systems. The fault current injections of IBDERs are limited to between 1-2 p.u. of the rated current. The combination of the limited fault current and varying system load makes it increasingly difficult to reliably set the overcurrent protection. To address this challenge, researchers are proposing adaptive overcurrent protection mechanisms that can adapt to changing system conditions. Accurate modeling of IBDERs is required to develop reliable protection schemes. This paper presents a photovoltaics-based IBDER model in OpenDSS for adaptive overcurrent protection. The paper presents the validation of the proposed IBDER model against a detailed model in electromagnetic transient programs. Protection analysis of the Electric Power Research Institute J1 feeder with the proposed IBDER model is also presented.

adaptive↗
DOE OSTI2022

Generating Sequential PV Deployment Scenarios for High Renewable Distribution Grid Planning: Preprint

This paper introduces a novel approach for generating solar photovoltaic (PV) plant deployment scenarios for grid integration planning. The approach guarantees consistency among scenarios of the same deployment by ensuring that higher penetration scenarios contain PV units deployed in lower penetration scenarios. It also constrains the size and spatial distribution of the PV plants and considers three placement types. A case study on a real-world distribution system proves that the precepts of scenario consistency, deployment diversity, and placement are met. The study further investigates the impact of the resulting scenarios via a stochastic hosting capacity analysis. Results indicate that the ratio between PV and load sizes, referred to as the nodal PV penetration factor (NPPF), is a key driver of the grid integration impact. By reducing the NPPF from 5 to 2, the maximum hosting capacity increased by at least 112%. The study also reveals that scenarios under random placement can lead to higher hosting capacity values.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2021

A Full Spectrum of Characterization for Insight into Carbon Speciation and Removal on a Cu/BEA Catalyst During Renewable High-Octane Hydrocarbon Synthesis

Conversion of methanol and dimethyl ether to high-octane gasoline catalyzed by beta zeolite (BEA) provides an opportunity for the production of high-quality fuels from renewable carbon sources (e.g., gasified biomass). Recent research demonstrated that a Cu-modified BEA zeolite catalyst (Cu/BEA) offered advantages over the unmodified BEA catalyst due to multifunctional Cu species that enabled incorporation of co-fed H2, reactivation of light alkanes, and reduction of products from the aromatic hydrocarbon pool. The shift in hydrocarbon pool chemistry has the potential to influence the identity and relative composition of surface carbon species that are often linked to deactivation. A detailed understanding of these carbon species is important to develop an effective and efficient regeneration procedure that can enable the transition from fundamental catalyst development to commercial application. Here, we applied complementary ex situ and in situ characterization techniques to compare the structures of surface carbon species on post-reaction Cu/BEA and unmodified BEA catalysts. Both catalysts contained acyclic and aromatic hydrocarbons along with graphitic carbon species. However, the post-reaction Cu/BEA catalyst had a lower polycyclic aromatic content, and further, the graphitic species were more hydrogenated and defective. It was also found that the presence of Cu promoted carbon removal at lower temperatures than for unmodified BEA through activation of O2 by Cu during thermal oxidation. The fundamental insight into the composition of surface carbon species enabled the design of an effective and efficient regeneration strategy for the DME homologation reaction over Cu/BEA, resulting in full recovery of the catalyst activity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Spectroscopic insight into carbon speciation and removal on a Cu/BEA catalyst during renewable high-octane hydrocarbon synthesis

Conversion of methanol and dimethyl ether (DME) to high-octane gasoline catalyzed by beta zeolite (BEA) provides an opportunity for the production of high-quality fuels from renewable carbon sources (e.g., gasified biomass). Recent research demonstrated that a Cu-modified BEA zeolite catalyst (Cu/BEA) offered advantages over the unmodified BEA catalyst due to multifunctional Cu species that enabled incorporation of co-fed H2, reactivation of light alkanes, and reduction of products from the aromatic hydrocarbon pool. The shift in hydrocarbon pool chemistry has the potential to influence the identity and relative composition of surface carbon species that are often linked to deactivation. A detailed understanding of these carbon species is important to develop an effective and efficient regeneration procedure that can enable the transition from fundamental catalyst development to commercial application. Here, we applied complementary ex situ and in situ characterization techniques to compare the structures of surface carbon species on post-reaction Cu/BEA and unmodified BEA catalysts. Both catalysts contained acyclic and aromatic hydrocarbons along with graphitic carbon species. However, the post-reaction Cu/BEA catalyst had a lower polycyclic aromatic content, and further, the graphitic species were more hydrogenated and defective. It was also found that the presence of Cu promoted carbon removal at lower temperatures than for unmodified BEA through activation of O2 by Cu oxide during thermal oxidation. The fundamental insight into the composition of surface carbon species enabled the design of an effective and efficient regeneration strategy for the DME homologation reaction over Cu/BEA, resulting in full recovery of the catalyst activity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Value and Role of Pumped Storage Hydro under High Variable Renewables

A project team, led by GE Research, was tasked by the U.S Department of Energy (DOE) to study the value and role of pumped storage hydro (PSH) under high variable renewables. The study was majority funded by DOE’s Office of Energy Efficiency and Renewable Energy (EERE) through a program managed by EERE’s Water Power Technologies Office. The project team for the study consists of three organizations – GE Research, GE Energy Consulting, and GE Renewables.

13 HYDRO ENERGY↗
DOE OSTI2020

Impacts of High Variable Renewable Energy Futures on Electric-Sector Decision Making: Demand-Side Effects

Previous work by the Berkeley Lab describes how high shares of variable renewable energy (VRE) such as wind and solar power could change wholesale electricity price dynamics. These include the timing of when electricity is cheap or expensive, locational differences in the cost of electricity, and the degree of regularity or predictability in those costs. Many decentralized decision-makers on the demand-side may not yet have considered the implications of these possible future changes. In this report, we evaluate the sensitivity of a set of demand-side decisions to different levels of VRE penetration ranging from a low of 5-20% to a high of 40-50%. The analysis builds on hourly wholesale energy and capacity prices in different VRE scenarios for four wholesale markets in the United States for the year 2030 (CAISO, ERCOT, NYISO, and SPP). The principal question for this exploration is whether private and public electric-sector decisions that are made based on assumptions reflecting low VRE levels still achieve their intended objective in a high VRE scenario with 40-50% wind and solar? This scoping report evaluates the impacts of changing patterns of peak system needs on the benefits of demand reductions by examining the altered value of different energy efficiency (EE) measures. Similarly, we investigate new opportunities for large energy consumers that may arise from periods with very low wholesale electricity prices. We calculate the value of new process investments (e.g., hydrogen production and other generalized electro-commodities), showcase the varying value of new product storage investments (such as reservoir extensions at a desalination plant), and estimate the benefits of increased process flexibility that uses electricity as a process-input in addition to traditional fossil fuels (e.g., district energy systems). Finally, many decentralized decision-makers and end-use customers are not directly exposed to wholesale electricity prices but instead receive price signals from their retail electricity rates. As wind and solar shares increase, we compare the economic efficiency of flat retail rates relative to more dynamic time-of-use tariffs with and without critical peak-pricing events.

14 SOLAR ENERGY↗
DOE OSTI2021

Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids

As variable renewable energy penetration increases beyond 80%, clean power systems will require long-duration energy storage or flexible, low-carbon generation. Here, we provide a detailed techno-economic evaluation and uncertainty analysis of applicable technologies and identify challenges and opportunities to support electric grid planning. We show that for a 120-h storage duration rating, hydrogen systems with geologic storage and natural gas with carbon capture are the least-cost low-carbon technologies for both current and future capital costs. These results are robust to uncertainty for the future capital cost scenario, but adiabatic compressed air and pumped thermal storage could be the least-cost technologies in the current capital cost scenario under uncertainty. Finally, we present a new storage system using heavy-duty vehicle fuel cells that could reduce the levelized cost of energy by 13%–20% compared with the best previously considered storage technology and, thus, could help enable very high (>80%) renewable energy grids.

25 ENERGY STORAGE↗