Cost-optimal wave-powered persistent oceanographic observation
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Cross-flow turbines (CFTs) are inherently unsteady devices with regards to operating principle and loading. By improving our understanding of the dynamic loading on these turbines, we hope to better inform CFT design, improve survivability, and reduce overall costs. The University of New Hampshire (UNH) and the National Renewable Energy Laboratory (NREL) collaborated on a project to instrument and test a four-bladed New Energy Corp. vertical axis cross-flow turbine in a real tidal flow. One blade from the 3.2 m diameter x 1.7 m height turbine was instrumented with eight full-bridge strain gauges along the span of the blade. The turbine was then deployed at the UNH-Atlantic Marine Energy Center (AMEC) Tidal Energy Test Site in Portsmouth, NH. Time-synchronized measurements of blade strain, inflow, thrust, rotational speed, and electrical output were obtained to characterize blade loading under various conditions. The blade strain was examined to assess the dynamic loading and conduct a fatigue analysis on the device.
Cook Inlet in Alaska is the most promising location for tidal energy development in the U.S. due to its significant tidal range of approximately 10 meters and high volume flux. The inlet's unique geometry and flow characteristics make it the most energetic tidal stream in the nation, with GW-scale potential energy capacity. With the growing interest in tidal energy converter (TEC) deployment in this area, we implemented a regional-scale, 3D hydrodynamic modeling framework to predict tidal current and turbulence characteristics that can assist TEC designers and project managers. We validated the model results extensively using various datasets collected with bottom-mounted acoustic Doppler current profilers and velocimeters. The comparison between the model outputs and observational data highlighted the effectiveness of the 3D FVCOM model and the Mellor-Yamada Level 2.5 Turbulence Model in accurately assessing macro-scale kinetic energy, turbulence intensity, and the production and dissipation rates at a prospective TEC site. Using two months of model simulation data, we examined the channel cross-section for TEC deployment, focusing on undisturbed power density and macro-scale turbulent properties. Further, our findings indicate that understanding the turbulence characteristics and flow properties can enhance Stage I/II resource characterization by identifying optimal locations for TECs and their layouts within the channel. Furthermore, we demonstrated that TEC designers can utilize macro-scale turbulence data from 3D coastal models as boundary conditions for other turbulence models, allowing for a more detailed resolution of the turbulence structure at TEC siting locations. Ultimately, this work emphasizes the importance of estimating flow and turbulence conditions in energetic systems to understand turbulent sites better and improve resource characterization.
Ocean Thermal Energy Conversion (OTEC) offers a promising renewable energy solution through a heat exchange process using the temperature difference between warm surface seawater and cold deep seawater. Because accurate resource characterization is critical for the optimal design and implementation of OTEC systems, a high-resolution numerical model is employed to better characterize the OTEC resource at Kona, Hawaii. Our model provides detailed spatial and temporal variability of the thermal gradient, which is essential for assessing the viability and efficiency of OTEC systems. The model results reveal distinct patterns and dynamics not captured by existing observations or models (e.g., lower-resolution information). These findings highlight the importance of using high-resolution models for accurate predictions of thermal gradient variability, ultimately supporting more efficient and sustainable OTEC deployment. Additionally, the study investigates the impacts of mixed water discharge from OTEC plants that can cause shock to organisms living in the surface water and potentially destabilize the water column. Understanding these effects is vital for minimizing any potential negative environmental consequences and ensuring the long-term viability of OTEC operations. Further, our model improves OTEC resource characterization, which can lead to optimal design and deployment of OTEC systems. The analysis of OTEC water discharge impacts can accelerate the development of OTEC technologies, overcoming permitting/consenting challenges. These findings contribute to the broader adoption of high-resolution modeling in ocean energy resource characterization, particularly for OTEC applications.
Sustainable and energy efficient municipal solid waste (MSW) management is vital for the health and performance of deployed soldiers, as they play an important role in the safety and dignity of a country. As the United States targets net-zero carbon-free emission goals, research and development are essential to surpassing current practices for management of MSW at military installations, both at home and abroad. To achieve this, it is important to act upon and implement the policies and instructions set by government agencies such as the Environmental Protection Agency (EPA), the Department of Energy (DOE), and the Department of Defense (DoD). Understanding the composition of waste and disposal methods that are in use at military installations is a crucial step to success. Here, the authors show this through analyzed data from trusted sources, such as government agencies, reports, magazines, journals, and other publications on MSW management techniques. By defining these challenges, possible solutions can be better understood for improved management of MSW at military installations in the United States. Finally, an integrated approach with deployable units capable of waste-to-energy technology using waste reduction, diversion, and recycling strategies shows to be an effective pathway for MSW conversion into energy and other useful products.
Decarbonizing vehicle production is essential to reducing automotive sector emissions. This study quantifies greenhouse gas (GHG) emissions from aluminum and steel auto-body sheet components produced in the US. It evaluates the effectiveness of circular economy (CE) strategies (greater closed-loop recycling of pre-consumer scrap, post-consumer scrap, and increased manufacturing yields) to reduce supply chain emissions across different process technology and electricity grid decarbonization pathways. We combine dynamic material flow analysis (2025–2050) with cradle-to-gate life-cycle modeling to assess production emissions and the potential reductions associated with the CE strategies under frozen, moderate, and aggressive technology and grid decarbonization scenarios. Current emissions intensities are estimated at approximately 12.3 kg.CO₂eq/kg of aluminum and 4.3 kg.CO₂eq/kg of steel sheet embedded in the vehicle. Under the frozen decarbonization scenario and current levels of circularity, annual emissions attributable to US aluminum and steel auto-body sheet supply chains could rise by 54 % and 18 % respectively by 2050. Rapid deployment of the CE strategies can cut these annual emissions in 2050 by 52 % for aluminum and 44 % for steel. However, scrap quality constraints lead to saturation points, limiting these benefits unless addressed. Aggressive deployment of low-carbon production technologies and a low-carbon grid reduces the relative benefit of implementing the CE strategies; however, even under the aggressive technology and grid decarbonization scenario, the CE strategies reduce annual emissions by a further 23 %-54 % by 2050. These findings highlight the urgent need to integrate CE strategies into the sheet metal supply chain to support decarbonization efforts and help meet climate targets.
Buildings contribute 40% of global greenhouse gas emissions; therefore, strategies that can substantially reduce emissions from the building stock are key components of broader efforts to mitigate climate change and achieve sustainable development goals. Models that represent the energy use of the building stock at scale under various scenarios of technology deployment have become essential tools for the development and assessment of such strategies. Within the past decade, the capabilities of building stock energy models have improved considerably, while model transferability and sharing has increased. Given these advancements, a new scheme for classifying building stock energy models is needed to facilitate communication of modeling approaches and the handling of important model dimensions. In this article, we present a new building stock energy model classification framework that leverages international modeling expertise from the participants of the International Energy Agency's Annex 70 on Building Energy Epidemiology. Drawing from existing classification studies, here we propose a multi-layer quadrant scheme that classifies modeling techniques by their design (top-down or bottom-up) and degree of transparency (black-box or white-box); hybrid techniques are also addressed. The quadrant scheme is unique from previous classification approaches in its non-hierarchical organization, coverage of and ability to incorporate emerging modeling techniques, and treatment of additional modeling dimensions. The new classification framework will be complemented by a reporting protocol and online registry of existing models as part of ongoing work in Annex 70 to increase the interpretability and utility of building stock energy models for energy policy making.
As more distributed energy resources are deployed on electric grid systems across the world, it is important to identify, characterize, and quantify the value elements of different types of distributed energy resources so that policymakers, developers, and utilities can make informed energy deployment decisions. This paper focuses on the value of wind energy as a distributed energy resource (i.e., “distributed wind”). Because of a lack of distributed wind-specific valuation studies, in this review we document the current state of distributed energy resource valuation, analyze a wide array of distributed energy resource valuation metastudies, and identify several value elements for which we recommend developing more robust and standardized calculation methodologies for their potential inclusion in distributed wind valuation. These value elements are ancillary services and locational, resilience, reliability, and resource diversity benefits. Furthermore, this work lays the foundation for a future comprehensive framework for distributed wind valuation studies.
Thermal gradient energy-generation technologies for powering unmanned underwater vehicles (UUVs) or autonomous sensing systems in the ocean are mainly in the research development phase or commercially available at a limited scale, and salinity-gradient energy-generation technologies have not been adequately researched yet. The demand for self-powered UUVs suitable for long-term deployments has been growing, and further research related to small-scale ocean gradient energy systems is needed. In this study, we conducted a comprehensive review about harvesting energy from ocean thermal or salinity gradients for powering UUVs, focusing on gliders and profiling floats. Thermal gradient energy systems for UUVs based on phase change materials (PCM) cannot provide the energy required for powering autonomous sensing systems because of the systems' low energy conversion efficiency. Besides reducing energy consumption by developing more efficient electrical-mechanical systems, enhancing the thermal conductivity of the PCMs may help address this challenge by increasing the power generation rate of the UUVs. Several other emerging technologies, such as thermoelectric generators, shape memory alloys, and small-scale thermodynamic cycle systems, have shown potential for powering UUVs, but they are still only at the laboratory testing or conceptual design phase. The most advanced power generation technologies based on salinity gradients, reverse electrodialysis and pressure-retarded osmosis, are still not economically viable for large-scale deployment, mainly because of the high cost of the components required to operate in harsh saline environments. Our feasibility evaluation showed that existing salinity gradient power generation technologies are not directly feasible for powering UUVs in the open ocean.
High penetration of renewable and sustainable Distributed Energy Resources (DER) into the traditional distribution system requires a well-coordinated control strategy for the improvement of system-wide reliability and resiliency. Implementation of such a holistic control architecture requires a flexible, near real-time, and bi-directional communication framework for facilitating the participation of various agents in a multi-vendor heterogeneous smart grid. While the sustainability of energy generation is ensured, this exposes the smart grid to extrinsic cyber threats, and appropriate defense mechanism(s) must be deployed to guarantee continued reliability and resiliency of the power grid. Further, the comprehensive literature review presented in this paper discusses the latest trends in the DER control schemes with fast communication requirements and their accompanying cyber–physical vulnerabilities. These control schemes are compared and contrasted for various traits. A three-level DER system architecture has been depicted, facilitating the deployment of these control schemes. The current developments of standard communication protocols, key security mechanisms, and best practices along major standards and guidelines are explored. The impacts of different attack types with miscellaneous DER functions based on various control schemes and associated mitigation solutions are also provided. Finally, challenges and future research directions for limiting cyber-power susceptibility to enhance resiliency are summarized. The work presented here will help us enabling a cyber-resilient and sustainable smart electric grid.
In this review, we examine state-of-the-art developments in integrating phase change materials (PCMs) for thermal energy storage (TES) in domestic heat pump water heaters (HPWHs). The component design optimization and control optimization of HPWHs and TES are reviewed for insight into improving the thermal capacity and efficiency of a PCM-integrated HPWH. The state-of-the-art review is categorized by the stage of development of the PCM for deployment in HPWHs. To select appropriate PCMs for HPWHs, a six-factor down-selection process is used to determine the best material(s) for integration in HPWHs with appropriate heat exchanger design. Ultimately, food-grade PCMs appear to be the best candidate for integration of TES in domestic HPWHs because they are nontoxic, highly cyclable, and have heat transfer properties accommodable to water heating when integrated in a manner to overcome the thermal conductivity limitations of the material. A key parameter of water heating performance is thermal heating power of the PCM component, which is not often reported. Many studies report significant improvement in capacity and efficiency. Furthermore, many performance metrics are identified from the literature to quantify the system performance, but agreement across studies is not found. Unified energy factor and first hour rating performance tests are required for commercially available HPWHs, and these performance metrics could homogenize the literature. Ultimately, we find that select PCMs are ready for deployment with HPWHs for performance improvement, and component design and control optimizations are new avenues of research and development required for a commercially viable PCM-integrated HPWH system.
Plate heat exchangers are commonly used in various industrial applications, such as refrigeration, air conditioning, heat pumps, powerplants, and chemical industries. Plate heat exchangers are well known for their superior heat transfer performance, compactness, and low refrigerant charge. Despite offering several advantages, they suffer from flow maldistribution issues. The flow maldistribution can deteriorate both the heat transfer and pressure drop performance, ultimately resulting in a lower system efficiency where plate heat exchangers are deployed. The flow maldistribution issues become more pronounced when the heat exchanger size is relatively large and the number of plates is higher, limiting the deployment of plate heat exchangers in larger industrial systems. Consequently, analyzing and understanding the flow maldistribution behavior in plate heat exchangers and finding ways to mitigate flow maldistribution related issues become essential topics of interest. Further, this review aims to address the effect of flow maldistribution on plate heat exchanger characteristics. First, the experimental and numerical works on flow maldistribution under single-phase and two-phase conditions are detailed. Subsequently, the end-channel and end-plate effects are discussed. Then, the methods to mitigate flow maldistribution in plate heat exchangers are outlined. Finally, based on a thorough literature survey and industrial requirements, future research directions are recommended.
Wildfires are increasingly interacting with electric power systems through a two-way hazard chain: fires damage grid assets and trigger cascading outages, while grid faults can ignite new fires under hot, dry, and windy conditions. This review synthesizes the state of knowledge across five domains: (i) physical impacts of flames, heat, and smoke on lines, towers, insulators, and substations; (ii) power-infrastructure-initiated ignitions via conductor clash, high-impedance faults, and corona discharge; (iii) widespread blackouts and disproportionate societal impacts; (iv) multi-scale monitoring spanning laboratory tests, in-situ and grid-integrated sensors, and Earth observation; (v) coupled modeling that links fire behavior with grid operations; and (vi) technological and strategic mitigation pathways spanning prevention, response, and recovery. We integrate these domains into a novel 'feedback-aware' socio-technical framework. Through a longitudinal analysis (2005-2025) of global incidents, we identify that while vegetation contact remains the most frequent ignition source, aging infrastructure failure has emerged as a critical driver of catastrophic 'mega-fires'. We further identify persistent gaps, including limited interoperability of high-frequency grid and environmental data, scarce real-time data assimilation, and under-developed equity metrics for outage management. We conclude by outlining a research agenda to (1) deploy interoperable sensing architectures, (2) advance feedback-coupled fire-grid simulations, and (3) evaluate mitigation portfolios through techno-economic and fairness lenses. Recognizing wildfire-grid interactions as coupled socio-technical systems is essential for protecting infrastructure and communities and for ensuring reliable, sustainable electricity in a changing world.
Considerable attention has recently focused on the vast energy and water demands of supercomputing, namely large-scale data centers that underpin artificial intelligence (AI), one of the great disruptors of contemporary society. Looking ahead some years from now, quantum computing is poised to disrupt established computing paradigms once again. Scientists and engineers are now working intensely to bring this century-old dream of physicists to fruition. Yet, as quantum computers begin to be integrated with classical supercomputing architectures, the implications for energy and physical resource use also need to be understood, especially how they compare to today’s AI data centers. These impacts have not yet been quantified by the research community – a notable gap in the literature, even if commercial-scale deployment of Quantum-Accelerated Computing Infrastructure (QuACI) is not expected for a few more years. This study is the first to conduct such an assessment. Using publicly available information from academic sources and private industry, we characterize multiple configurations of superconducting qubit-based, fault-tolerant quantum computers (FTQC) that could plausibly be deployed at scale in the 2030s and into the 2040s. By parameterizing these FTQC systems at a process level, we conduct a prospective scenario analysis to quantify their energy and physical resource needs. While these estimates are uncertain, given the current state of quantum technologies and their unknown future trajectories, important insights can already be drawn. One key finding is that while the electricity needs for a fleet of FTQCs are within the bounds of previous modeling studies that have explored high electricity demand futures, the needs for certain physical resources, namely water and helium-3, could pose bottlenecks to QuACI scale-up.
Marine renewable energy (MRE) harnesses ocean-based resources such as waves, tides, currents, and thermal or salinity gradients for sustainable power generation. It has the potential to complement existing renewable resources, support remote communities, and contribute to decarbonization efforts. However, understanding the hydrodynamic forces created by MRE devices and their impacts on marine life is critical for responsible deployment. Here, to address these concerns, advanced sensor devices, including the Marine Sensor Fish (MSF), Sensor Fish Mini (SF Mini), and Flexible Sensor Fish (FSF), were developed to measure interactions between aquatic organisms and MRE systems. This paper details the design, manufacturing, calibration, and field deployment of these sensor suites, highlighting their ability to capture key physical stressors such as shear forces, pressure changes, and collision impacts. The MSF successfully evaluated turbine interactions at a tidal turbine in the Salish Sea, capturing data on turbulence, collision impact, and pressure gradients. The SF Mini validated hydrodynamic conditions in scaled hydraulic models, supporting computational fluid dynamics simulations. The FSF, with its flexible silicone body, measured species-specific impacts in turbulent environments. This research demonstrates the potential of Sensor Fish technology to advance sustainable marine energy systems by reducing biological impacts and informing environmentally sustainable designs.
Hydropower’s ability to quickly adapt to variability from wind and solar generation by fluctuation flow rates can allow the electricity grid to integrate more renewable capacity. However, these rapid flow fluctuations, required to meet variability needs, can negatively impact aquatic ecosystems. In this study, we quantified energy-economic-environment tradeoffs at five conventional hydropower facilities (i.e. hydropower produced ad a dam on a river channel) across the United States to identify a mix of operational regimes that can provide flexibility to support variable renewable energy integration and environmental protections. Model results show a range of ability to meet demand from 4.7% to 97.8% depending on which case study is considered. Additionally, when modeling the case study facilities on a range of RoR conditions, allowing a % of inflow as discharge, we found the range of 140–200% of inflow allowed as discharge lead to lowest environmental impact while meeting the highest amount of demand. Our sensitivity analysis results demonstrated the Richard-Baker Flashiness Index, used to measure flowrate changes, and Revenue, were negatively correlated with the percent of hydropower generation within the defined Regional Energy Deployment System balancing area (i.e. region in which energy demand and energy supply is balanced based on the Regional Energy Deployment System model) yet positively correlated to the variable renewable energy generation percentage in the defined balancing area. In conclusion, our results suggest hydropower operations can aid in increasing renewable energy generation while limiting environmental impacts when considering a holistic analysis of energy-economic-environment tradeoffs.
The retail rate impacts of a number of emerging trends (e.g., rapid deployment of electric vehicles and storage, transmission build-out for large-scale renewables deployment, and grid modernization) are unknown. Importantly, decision-makers are concerned about the potential future rate impacts on energy affordability and equity. We disaggregate the key drivers of retail electricity rates and assess their impacts on future rate growth considering their interactions and uncertainty. Specifically, we develop ranges of future cost growth for a generic investor-owned and vertically-integrated electric utility representing typical cost and operating characteristics. The rate driver growth rate ranges are applied in isolation and jointly to quantify the uncertainty and variability in future retail electricity rates. The results identify what rate drivers and factors may minimize and/or decrease uncertainty in retail rate growth and their linkages to industry trends.