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At least 271 records · Page 15

Renewable Hydrogen Potential from Biogas in the United States

This analysis updates and expands upon previous biogas studies to include total potential and net availability of methane in raw biogas with respect to competing demands and includes a resource assessment of four sources of biogas: (1) wastewater treatment plants, including domestic and a new assessment of industrial sources; (2) landfills; (3) animal manure; and (4) a new assessment of industrial, institutional, and commercial sources. The results of the biogas resource assessment are used to estimate the potential production of renewable hydrogen from biogas as well as the fuel cell electric vehicles that the produced hydrogen might support.

08 HYDROGEN↗

Sea-Ice Effects on the Alaskan Wave Climate

This study investigates the uncertainty of wave resource assessments due to sea ice in the nearshore region of Alaska using an innovative approach of combining remotely sensed data and a third-generation phase-averaged wave model. Modeling analysis focused on winter months, when high wave energy is present along with sea ice. This study includes recommendations to improve the accuracy of wave resource assessments and optimize marine and hydrokinetic device siting with consideration of sea-ice effects. Additional recommendations, based on preliminary sea-ice information in Cook Inlet, Alaska, are provided for a trajectory analysis of drifting sea ice and a collision risk assessment of sea ice and tidal turbines. The outcome of this project will provide valuable information about the level of sea-ice impact on the accuracy of wave resource assessments in the Alaska nearshore region, and of sea-ice collision risk with MHK devices and ocean observation systems during winter months when sea ice is present.

16 TIDAL AND WAVE POWER↗

Assessment of Algal Farm Designs Using a Dynamic Modular Approach

The notion of renewable energy provides an important mechanism for diversifying an energy portfolio, which ultimately would have numerous benefits including increased energy resilience, reduction of foreign energy supplies, reduced GHG emissions, development of a green energy sector that contributes to economic growth, and providing a sustainable energy supply. The conversion of autotrophic algae to liquid transportation fuels is the basis of several decades of research to competitively bring energy-scale production into reality; however, many challenges still remain for making algal biofuels economically viable. Addressing current challenges associated with algal production systems, in part, requires the ability to assess spatial and temporal variability, rapidly evaluate alternative algal production system designs, and perform large-scale assessments considering multiple scenarios for thousands of potential sites. We introduce the Algae Logistics Model (ALM) which helps to address these challenges. The flexible nature of the ALM architecture allows the model to: 1) interface with external biomass production and resource assessment models, as well as other relevant datasets including those with spatiotemporal granularity; 2) interchange design processes to enable operational and economic assessments of multiple design configurations, including the integration of current and new innovative technologies; and 3) conduct trade-off analysis to help understand the site-specific techno-economic trade-offs and inform technology decisions. This study uses the ALM to investigate a baseline open-pond production system determined by model harmonization efforts conducted by the U.S. Department of Energy. Six sites in the U.S. southern-tier were sub-selected and assessed using daily site-specific algae biomass productivity data to determine the economic viability of large-scale open-pond systems. Results show that costs can vary significantly depending on location and biomass productivity and that integration of novel dewatering equipment, order of operations, and equipment scaling can also have significant impacts on economics.

09 BIOMASS FUELS↗

Investigation of Spatial Variation of Sea States Offshore of Humboldt Bay CA Using a Hindcast Model.

Spatial variability of sea states is an important consideration when performing wave resource assessments and wave resource characterization studies for wave energy converter (WEC) test sites and commercial WEC deployments. This report examines the spatial variation of sea states offshore of Humboldt Bay, CA, using the wave model SWAN . The effect of depth and shoaling on bulk wave parameters is well resolved using the model SWAN with a 200 m grid. At this site, the degree of spatial variation of these bulk wave parameters, with shoaling generally perpendicular to the depth contours, is found to depend on the season. The variation in wave height , for example, was higher in the summer due to the wind and wave sheltering from the protruding land on the coastline north of the model domain. Ho wever, the spatial variation within an area of a potential Tier 1 WEC test site at 45 m depth and 1 square nautical mile is almost negligible; at most about 0.1 m in both winter and summer. The six wave characterization parameters recommended by the IEC 6 2600 - 101 TS were compared at several points along a line perpendicular to shore from the WEC test site . As expected, these parameters varied based on depth , but showed very similar seasonal trends.

Dallman, Ann Renee↗

Advancements in 3D Structural Analysis of Geothermal Systems

Robust geothermal activity in the Great Basin, USA is a product of both anomalously high regional heat flow and active fault-controlled extension. Elevated permeability associated with some fault systems provides pathways for circulation of geothermal fluids. Constraining the local-scale 3D geometry of these structures and their roles as fluid flow conduits is crucial in order to mitigate both the costs and risks of geothermal exploration and to identify blind (no surface expression) geothermal resources. Ongoing studies have indicated that much of the robust geothermal activity in the Great Basin is associated with high density faulting at structurally complex fault intersection/interaction areas, such as accommodation/transfer zones between discrete fault systems, step-overs or relay ramps in fault systems, intersection zones between faults with different strikes or different senses of slip, and horse-tailing fault terminations. These conceptualized models are crucial for locating and characterizing geothermal systems in a regional context. At the local scale, however, pinpointing drilling targets and characterizing resource potential within known or probable geothermal areas requires precise 3D characterization of the system. Employing a variety of surface and subsurface data sets, we have conducted detailed 3D geologic analyses of two Great Basin geothermal systems. Using EarthVision (Dynamic Graphics Inc., Alameda, CA) we constructed 3D geologic models of both the actively producing Brady’s geothermal system and a ‘greenfield’ geothermal prospect at Astor Pass, NV. These 3D models allow spatial comparison of disparate data sets in 3D and are the basis for quantitative structural analyses that can aid geothermal resource assessment and be used to pinpoint discrete drilling targets. The relatively abundant data set at Brady’s, ~80 km NE of Reno, NV, includes 24 wells with lithologies interpreted from careful analysis of cuttings and core, a 1:24,000 scale detailed geologic map and cross-sections, 2D seismic reflection profiles and other geophysical data, and downhole temperature data. The 3D geologic model based on these data consists of 61 fault planes, 25 distinct stratigraphic units, and 2 intrusive bodies. Geothermal fluids are produced from a left step-over/relay ramp within the Brady’s Fault Zone (BFZ). Under local stress conditions, fault segments that strike NNE-to-NE are most likely to slip and/or dilate, and therefore transmit geothermal fluids. The 3D model defines the locations of discrete fault intersections within the BFZ and indicates that the densest zones of structurally controlled fracture permeability are ~10-to-10s of meters in diameter and plunge ~55° NW-NNW beneath the heart of the BFZ step over. The locations of high intersection density, high fault slip and dilation tendency, high subsurface temperature, and lithologies known to support high fracture permeability are combined to produce 3D ‘fairway’ maps useful in both assessments of geothermal resource potential and for defining drilling targets. Astor Pass is located on the Pyramid Lake Paiute Reservation, ~80 km north of Reno, NV. It is a prospective ‘greenfield’ geothermal area, and thus subsurface data are relatively sparse. Available data include: two relatively deep wells (~1400 m) and one shallower well (~500 m) with lithologies interpreted from drill cuttings, several 2D seismic reflection profiles, a 1:24,000 scale geologic map and cross-section, a shallow temperature survey, and downhole temperature data. 3D modeling based on these data has defined 19 distinct fault planes and 16 stratigraphic units. Based on the stress field calculated from borehole breakouts, drilling induced tensile cracks and petal-centerline cracks in the two relatively deep wells, 3D slip and dilation tendency analysis indicates that northerly striking fault segments are most likely to slip and/or dilate, and therefore transmit geothermal fluids. Analysis of fault intersection density indicates that the highest density of structurally controlled permeability within the field lies in a narrow (10-to-10s of m) zone plunging moderately (~35°) to the NNW beneath Pleistocene tufa deposits. This zone of increased fracture density, which we interpret as the primary upflow zone, is controlled by the intersection of N-to-NNW striking normal faults and a WNW striking dextral fault zone and represents the most promising target for future drilling. Construction of a 3D geologic model involves integration of a variety of data into an internally consistent framework. A robust model allows for spatial comparison between the various types of data (structural, stratigraphic, geophysical, temperature, etc.) that are commonly used independently to site geothermal wells. Furthermore, highly detailed 3D geologic models provide the basis for additional quantitative analysis, including 3D fault slip and dilation tendency analysis and the precise location of structurally controlled permeability pathways. These analyses provide detailed information relating to the internal dynamics of geothermal systems and can mitigate the costs and risks of geothermal exploration and development by contributing to better well targeting and more accurate evaluations of resource potential.

15 GEOTHERMAL ENERGY↗

AATTENUATION—The Atmospheric Attenuation Model for CSP Tower Plants: A Look-Up Table for Operational Implementation

Attenuation of solar radiation between the receiver and the heliostat field in concentrated solar power (CSP) tower plants can reduce the overall system performance significantly. The attenuation varies strongly with time and the average attenuation at different sites might also vary strongly from each other. If no site specific attenuation data is available, the optimal plant design cannot be determined and rough estimations of the attenuation effect are required leading to high uncertainties of yield analysis calculations. The attenuation is caused mainly by water vapor content and aerosol particles in the lower atmospheric layer above ground. Although several on-site measurement systems have been developed during recent years, attenuation data sets are usually not available to be included during the plant project development. An Atmospheric Attenuation (AATTENUATION) model to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity, and barometric pressure measurements was developed and validated by the authors earlier. The model allows the accurate estimation of attenuation for sites with low attenuation and gives an estimation of the attenuation for less clear sites. However, the site-dependent coefficients of the AATTENUATION model had to be developed individually for each site of interest, which required time-consuming radiative transfer simulations, considering the exact location and altitude, as well as the pre-dominant aerosol type at the location. This strongly limited the application of the model despite its typically available input data. In this manuscript, a look-up table (LUT) is presented which enables the application of the AATTENUATION model at the site of interest without the necessity to perform the according complex radiative transfer calculations for each site individually. This enables the application of the AATTENUATION model for virtually all resource assessments for tower plants and in an operational mode in real time within plant monitoring systems around the world. The LUT also facilitates the generation of solar attenuation maps on the basis of long-term meteorological data sets which can be considered during resource assessment for CSP tower plant projects. The LUTs are provided together with this manuscript as supplementary files. The LUT for the AATTENUATION model was developed for a solar zenith angle (SZA) grid of 1°, an altitude grid of 100 m, 7 different standard aerosol types and the standard AFGL atmospheres for mid-latitudes and the tropics. The LUT was tested against the original version of the AATTENUATION model at 4 sites in Morocco and Spain, and it was found that the additional uncertainty introduced by the application of the LUT is negligible. With the information of latitude, longitude, altitude above mean sea level, DNI, relative humidity (RH), ambient temperature (Tair), and barometric pressure (bp), the attenuation can be now derived easily for each site of interest.

14 SOLAR ENERGY↗

The Marine and Hydrokinetic ToolKit for Data Quality Control and Analysis: Preprint

The ability to handle data is critical at all stages of marine energy (ME) development. The marine hydrokinetic toolkit (MHKiT) is an open-source marine energy software, which includes modules for ingesting, applying quality control, processing, visualizing, and managing data. MHKiT-Python and MHKiT-MATLAB provide robust and verified functions that are needed by the ME community to standardize data processing. Calculations and visualizations adhere to International Electrotechnical Commission (IEC) technical specifications and other guidelines. A resource assessment of NDBC buoy 46050 near PACWAVE is performed using MHKiT and discusses comparisons to the resource assessment provided performed by Dunkel et al.

marine energy↗

The Marine and Hydrokinetic Toolkit (Mhkit) for Data Quality Control and Analysis

The ability to handle data is critical at all stages of marine energy (ME) development. The marine hydrokinetic toolkit (MHKiT) is an open-source marine energy software, which includes modules for ingesting, applying quality control, processing, visualizing, and managing data. MHKiT-Python and MHKiT-MATLAB provide robust and verified functions that are needed by the ME community to standardize data processing. Calculations and visualizations adhere to International Electrotechnical Commission (IEC) technical specifications and other guidelines. A resource assessment of NDBC buoy 46050 near PACWAVE is performed using MHKiT and discusses comparisons to the resource assessment provided performed by Dunkel et al.

marine energy↗

Atmospheric Transmittance Model Validation for CSP Tower Plants

In yield analysis and plant design of concentrated solar power (CSP) tower plants, increased uncertainties are caused by the mostly unknown solar attenuation between the concentrating heliostat field and the receiver on top of the tower. This attenuation is caused mainly by aerosol particles and water vapor. Various on-site measurement methods of atmospheric extinction in solar tower plants have been developed during recent years, but during resource assessment for distinct tower plant projects in-situ measurement data sets are typically not available. To overcome this lack of information, a transmittance model (TM) has been previously developed and enhanced by the authors to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity and barometric pressure measurements. Previously the model was only tested at one site. In this manuscript, the enhanced TM is validated for three sites (CIEMAT’s Plataforma Solar de Almería (PSA), Spain, Missour, Morocco (MIS) and Zagora, Morocco (ZAG)). As the strongest assumption in the TM is the vertical aerosol particle profile, three different approaches to describe the vertical profile are tested in the TM. One approach assumes a homogeneous aerosol profile up to 1 kilometer above ground, the second approach is based on LIVAS profiles obtained from Lidar measurements and the third approach uses boundary layer height (BLH) data of the European Centre for Medium-Range Weather Forecasts (ECMWF). The derived broadband transmittance for a slant range of 1 km (T 1$km$ ) time series is compared with a reference data set of on-site absorption- and broadband corrected T 1$km$ derived from meteorological optical range (MOR) measurements for the temporal period between January 2015 and November 2017. The absolute mean bias error (MBE) for the TM’s T 1$km$ using the three different aerosol profiles lies below 5% except for ZAG and one profile assumption. The MBE is close to 0 for PSA and MIS assuming a homogeneous extinction coefficient up to 1 km above ground. The root mean square error (RMSE) is around 5–6% for PSA and ZAG and around 7–8% for MIS. The TM performs better during summer months, during which more data points have been evaluated. This validation proves the applicability of the transmittance model for resource assessment at various sites. It enables the identification of a clear site with high T 1$km$ with a high accuracy and provides an estimation of the T 1$km$ for hazy sites. Thus it facilitates the decision if on-site extinction measurements are necessary. The model can be used to improve the accuracy of yield analysis of tower plants and allows the site adapted design.

14 SOLAR ENERGY↗

Hawai‘i Play Fairway (Final Report)

Most of Hawai'i's geothermal resources are blind—their manifestations, such as hot springs and steam vents, do not appear on the ground surface because the heated water flows far below. With the exception of $K\bar{i}lauea East Rift Zone$, in most areas of Hawai'i, high lateral permeability in the first kilometer below ground surface prevents surface thermal features from developing. As a methodology for discovering these blind resources, Play Fairway Analysis (PFA) involves finding potential locations of blind hydrothermal systems and describing potential geothermal sources in rift-zone settings. Using the PFA to find Hawai'i's geothermal resources, the University of Hawai'i (UH) conducted the Hawai‘i Play Fairway Project, Hawai'i's first statewide geothermal resource assessment since 1985. Sponsored by the U.S. Department of Energy, the Hawai'i Play Fairway Project provided an updated resource assessment, a roadmap for additional exploration activities, and the identification of areas for further exploration. Benefitting from UH's core competency in earth sciences and experienced geothermal researchers, the project comprised three phases. During the first phase, the team identified, compiled, and ranked existing geologic, groundwater, and geophysical datasets relevant to subsurface heat, fluid and permeability. Using a Bayesian statistical approach, the team developed a statistical methodology to integrate these data into a resource probability map. The team evaluated the confidence in the probability value and considered development viability of areas with geothermal resources. With these analyses, the team identified 10 locations in the Hawaiian Islands for exploration activities. For the second phase, the team collected new groundwater data in 10 locations across the state and new geophysical data on $L\bar{a}na‘i, Maui$, and central Hawai'i Island and modeled topographically induced stress to better characterize subsurface permeability. Analyzing the subsurface stresses, the team evaluated the potential for fracture-induced permeability. The team inverted the MT and gravity data to produce 3D models of resistivity and density, respectively, on $L\bar{a}na‘i$, across $Haleakal\bar{a}'s$ SW rift (Maui), and surrounding Mauna Kea (Hawai‘i Island). The team developed and applied a new method for incorporating depth information about resistivity, density, and potential for fracture-induced permeability into the statistical method for computing resource probability in these three focus areas. The team incorporated the new groundwater results with the new geophysical results and the calculations of potential for fracture-induced permeability to produce updated maps of resource probability and confidence. Through combining data from the first and second phases, the team determined locations for further exploration during the third phase. For MT and gravity surveys, the team recommended $Kaua'i's$ $L\bar{i}hu'e$ $Basin$, the east rift of $Maui's$ $Haleakal\bar{a}$ volcano, and the southwest rift of Hawai'i Island's Mauna Loa volcano. The MT and gravity surveys aimed to enable improved confidence in the resource potential in these locations. For drilling deep groundwater well(s), the team recommended Southeast Mauna Kea and $L\bar{a}na's$ $P\bar{a}l\bar{a}wai$ $Basin$. During the third phase, further exploration involved drilling a groundwater well in $L\bar{a}na's$ $P\bar{a}l\bar{a}wai$ $Basin$ and performing more geophysical surveys. We deepened an existing water well proximal to our target area on $L\bar{a}na'i$ due to funding constraints that precluded us from spudding a new well that would exceed 1km depth. Drilling was preceded by a number of substantial elements including: writing an Environmental Assessment and the subsequent legal process, performance of deviation logging, lowering a camera down the well, coordinating site preparation with $P\bar{u}lama$ $L\bar{a}na'i$, shipping the UH-owned rig interisland, procuring supplies, and leading 3 community meetings on $L\bar{a}na'i$. Drilling occurred 24/7 the entire month of June 2019 over which time $L\bar{a}na'i$ $Well$ 10 was deepened from 427 m to 1057 m, with continuous core collected. We measured a roughly linear temperature gradient averaging 42°C/km and a maximum bottom hole temperature of 66°C. This gradient is more than twice the background for Hawai'i and within a range of gradients measured in this depth range for some exploration wells within KERZ. We consider these results encouraging for $L\bar{a}na'i's$ resource potential and recommend following with a slim hole within $L\bar{a}na'i's$ caldera (our target zone) to ~ 2 km. Further, the positive implications such results have for the island of O‘ahu are substantial - the shield stage of O'ahu's volcanoes ended 1-2 My earlier. However, O'ahu uses more electricity than the rest of the islands combined, and the utility recently called for 500-700MW of firm, dispatchable renewable electricity on O'ahu by 2033. In Phase 3, we also collected limited new encouraging groundwater data, and updated our thoughts on the probabilities of fluid and permeability at resource depths (Pr F = 1; Pr P = mostly unconstrained). Ultimately, we advocate for using our final probability of heat, and confidence in this probability, to drive the next phase of exploration. We contend further development of geothermal in Hawai‘i will enable the state to achieve its 100% renewable policy objective and Hawai'i to transition off of fossil fuels through geothermal discovery and development. The project not only produced a large amount of data and expanded the existing knowledge of Hawai'i's geothermal resources, but also produced publications, theses, presentations, core photos, datasets, media reports, television interviews, community events, and a blog. Students and new professionals benefitted from the project's hands-on research experiences and educational opportunities and earned awards and recognition.

15 GEOTHERMAL ENERGY↗

Clear Sky Tampa Bay: Project and Toolkit Overview - Resilience-Based Resources for Assessing Solar + Storage on Critical Facilities

The Clear Sky Tampa Bay Team, convened by the Tampa Bay Regional Planning Council (TBRPC) and composed of county and city partners, private and public sector stakeholders, solar energy experts, and academic partners, collaboratively developed a set of resources to support the expanded use of solar + storage in the context of community resilience in Florida. Key questions guiding the project included: (1) What types of buildings and systems provide the maximum resilience benefits? (2) Where within our communities should we prioritize future solar + storage analysis or investment to mitigate the impacts of disasters? (3) How might other societal variables be incorporated into a site assessment when considering solar + storage for resilience? (4) Can a standardized process be developed that leverages existing resources to assist local governments with solar + storage for resilience research and decision-making?

14 SOLAR ENERGY↗

Adversarial super-resolution of climatological wind and solar data

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50 × resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

14 SOLAR ENERGY↗

High-Resolution Regional Wave Hindcast for the U.S. West Coast

This report summarizes modeling efforts for the simulation of wave climate along the U.S. West Coast using an unstructured, nested-grid modeling approach that incorporates a global-regional nested grid using WAVEWATCH III® and the high-resolution UnSWAN (Unstructured-grid Simulating Waves Nearshore) model. Wave resource and long-term wave bulk parameters were simulated and subsequently validated with wave buoy data within the model domain. Overall, model results match observations at buoys well. The model hindcast was able to reproduce the seasonal variation of the sea state with large waves that occur in the winter and early spring months when wind forcing is strong and the calm sea state during the summer when wind forcing is weak. The nested-grid modeling framework employed in this study provides a powerful and efficient modeling approach to accurately simulate wave climate at regional and long-term temporal scales with sufficiently fine resolutions in the nearshore region. Results can be used to inform the deployment of wave energy converters and other nearshore instrumentation/devices in nearshore regions, assist in prioritizing hotspots for near-term market opportunities and development, and provide wave resource assessment data and information to inform resource estimates for marine and hydrokinetic energy technologies.

16 TIDAL AND WAVE POWER↗

Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets

At any site, the bankability of a projected solar power plant largely depends on the accuracy and general quality of the solar radiation data generated during the solar resource assessment phase. The term 'site adaptation' has recently started to be used in the framework of solar energy projects to refer to the improvement that can be achieved in satellite-derived solar irradiance and model data when short-term local ground measurements are used to correct systematic errors and bias in the original dataset. This contribution presents a preliminary survey of different possible techniques that can improve long-term satellite-derived and model-derived solar radiation data through the use of short-term on-site ground measurements. The possible approaches that are reported here may be applied in different ways, depending on the origin and characteristics of the uncertainties in the modeled data. This work, which is the first step of a forthcoming in-depth assessment of methodologies for site adaptation, has been done within the framework of the International Energy Agency Solar Heating and Cooling Programme Task 46 'Solar Resource Assessment and Forecasting.'

14 SOLAR ENERGY↗

A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation

Accurate estimation of the power curve for wind turbines or wind farms is crucial to ensure their efficient operation and management. However, conventional methods for power curve estimation rely either on expensive and infrequent measurements or on low-quality numerical simulations. Moreover, the majority of previous studies on power curve estimation for wind turbines or wind farms focused on deterministic estimation, which provides a point estimate of the relationship between wind speed and power generation. Nevertheless, the deterministic approach fails to consider the inherent uncertainty associated with wind energy production resulting from varying turbine characteristics. This can lead to inaccurate power generation estimation and suboptimal decisions regarding energy management. In this paper, a kernel density estimation (KDE) based Multi-Fidelity Gaussian Process Regression (MFGPR) model is proposed to fuse theoretical power curve data and the ground true measurements to create a mapping of wind speed and wind power. By conducting a case study on an actual wind farm in China, the efficacy of the proposed MFGPR model was demonstrated in characterizing the variability of wind power. The probabilistic MFGPR model was also able to generate confidence intervals that encompassed the measured power, thereby improving the accuracy and confidence in wind power estimation or wind resource assessment. Overall, the proposed MFGPR model offers a reliable approach to integrate high-fidelity ground measurements and theoretical power curve data, resulting in precise wind resource assessment and power estimation.

Gaussian process regression↗

Remote Sensing of Complex Flows by Doppler Wind Lidar: Issues and Preliminary Recommendations

Remote sensing of winds using lidar has become popular and useful in the wind energy industry. Extensive experience has been gained with using lidar for applications including land-based and offshore resource assessment, plant operations, and turbine control. Prepared by members of International Energy Agency Task 32, this report describes the state of the art in the use of Doppler wind lidar for resource assessment in complex flows. The report will be used as input for future recommended practices on this topic.

17 WIND ENERGY↗

An Overview of Wind Energy Production Prediction Bias, Losses, and Uncertainties

The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the industry is recording near-zero average energy prediction bias. Herein, we present an overview of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including the improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight the opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty, and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary on how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the manuscript.

literature review↗

Energy Recovery Hydropower: Prospects for Off-Setting Electricity Costs for Agricultural, Municipal, and Industrial Water Providers and Users; July 2017 - September 2017

Energy recovery hydropower is one of the most cost-effective types of new hydropower development because it is constructed utilizing existing infrastructure, and it is typically able to complete Federal Energy Regulatory Commission (FERC) review in 60 days. Recent changes in federal and state policy have supported energy recovery hydropower. In addition, some states have developed programs and policies to support energy recovery hydropower, including resource assessments, regulatory streamlining initiatives, and grant and loan programs to reduce project development costs. This report examines current federal and state policy drivers for energy recovery hydropower, reviews market trends, and looks ahead at future federal resource assessments and hydropower reform legislation.

13 HYDRO ENERGY↗