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DOE OSTI2023

Model predictive control of mixing controlled compression ignition operation for low reactivity fuels

Using gasoline or other low reactivity fuels with a pilot injection or port fuel injection in a compression ignition engine has shown great potential in reducing NOx emissions while keeping high thermal efficiency compared to diesel. However, excessive combustion noise is caused by a high maximum pressure rise rate in the cylinder due to the higher fractions of premixed charge of the low-reactivity fuel. This noise can result in structural damage to engine components and as such, combustion noise limits the range of the operating parameters and makes the control of such engines challenging. In this study, a simulation environment was built up in MATLAB/Simulink leveraging a physics-based zero-dimension combustion model to capture the in-cylinder pressure time traces as well as metrics relevant to thermal efficiency and combustion noise. Here, in order to also facilitate the control of emissions, machine learning models were investigated to capture NOx emissions. A kernel-based extreme learning machine (K-ELM) performed best and had a coefficient of correlation (R-squared) of 0.998. The combustion and NOx emission models are valid for not only conventional gasoline fuel but also oxygenated alternative fuel blends at three different pilot injection strategies. In order to track key combustion metrics while keeping noise and emissions within constraints, a model predictive control (MPC) was applied for a compression ignition engine operating with a range of potential fuels and fuel injection strategies. The MPC is validated under different scenarios, including a load step change, fuel type change, and injection strategy change, with proportional–integral (PI) control as the baseline. The simulation results show that MPC reduces about 26% of ringing intensity in the transient process and 17% at the steady state for E30. Generally, MPC can optimize the overall performance through modifying the main injection timing, pilot fuel mass, and exhaust gas recirculation (EGR) fraction.

42 ENGINEERING↗

DOE OSTI2023

A novel method for co-optimizing battery sizing and charging strategy of battery electric bus fleets: An application to the city of Paris

Battery-electric buses (BEBs) are a promising technology for replacing diesel buses and reducing their environmental burden. However, their charging process can take several hours, depending on the charging technique and strategy, making them susceptible to schedule disruptions. Furthermore, the selection of the charging strategy and battery size can increase the total capital investment and operational expenses of a BEB fleet, which is a major obstacle to its adoption. Therefore, to minimize the total cost of ownership (TCO) and prevent schedule disruptions, it is essential to establish a well-defined approach to determine an appropriate battery size and charging strategy for BEB fleets. This paper presents a method for reducing the TCO of BEB by determining the optimal battery size and charging strategy for each bus while satisfying operating constraints. The method involves a two-step optimization algorithm that uses Dynamic Programming and Genetic Algorithm. Further, the study applies this approach to the bus fleet serving line 21 in Paris and generates the optimal battery sizing, charging strategy, and required charging infrastructure. The results indicate that 100 kWh batteries offer the best trade-off between capital and operational expenditure for the fleet deployment if used with 65–85 kW chargers at bus terminals.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗

DOE OSTI2024

An OpenStreetMaps based tool to study the energy demand and emissions impact of electrification of medium and heavy-duty freight trucks

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from OSM, utility rate design data across the United States, and freight volume data, to determine these values. In order to deal with the computational complexity of this problem, we formulate the problem as a convex optimization problem that is scalable to a large geographic area. In our analysis, we further evaluate various scenarios of utility rate design (energy charges) and EV penetration rate across different geographic regions and their impact on the operating cost and emissions of the freight trucks. Our approach determines the net emissions reduction benefits of freight electrification by considering the primary energy source in different regions. Such analysis will provide insights to policy makers in designing utility rates for electric vehicle supply equipment (EVSE) operators depending upon the specific geographic region and to electric utilities in deciding infrastructure upgrades based on the spatial distribution of the added energy demand of M&HD EVs. To showcase the results, a case study for the U.S. state of Texas is conducted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Making coal relevant for small scale applications: Modular gasification for syngas/engine CHP applications in challenging environments

Small-scale coal gasification technology, coupled to a reciprocating engine generator, has the potential for making coal a cost competitive resource for meeting the flexible energy needs and resiliency requirements of utilities across the United States. To maintain grid stability and reliability, electrical generation must be regulated to match the load at the proper voltage and frequency. With the expansion of intermittent sources into the grid, such as wind and solar, frequency and voltage regulation become increasingly important and challenging. This work presents the results of a Front End Engineering Design (FEED) effort to detail the engineering and preliminary economics of a small-scale, air blown, fixed-bed gasification process, operating at near-atmospheric pressure, with gas cleanup to provide syngas and pyrolysis liquid fuels for use in reciprocating engine generators for combined heat and power at the University of Alaska Fairbanks. A very detailed assessment of capital and operating costs allows the evaluation of a levelized cost of electricity of 208.06 $/MWh, which can be reduced through the selling of by-products (steam for heating purposes and pyrolysis liquids to be used for power generation in an existing diesel engine). Here, a combined sensitivity analysis, based on the Monte Carlo approach, has been carried out to evaluate the effects of the uncertainties (capital and operating costs and plant annual availability) on the LCOE.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

Superior performance biodiesel from biomass-derived fusel alcohols and low grade oils: Fatty acid fusel esters (FAFE)

We demonstrated production of a superior performance biodiesel referred to here as fatty acid fusel alcohol esters (FAFE) – by reacting fusel alcohols (isobutanol, 3-methyl-1-butanol, and (S)-(-)-2-methyl-1-butanol) with oil (glyceryl trioleate) using lipase from Aspergillus oryzae. Reaction conditions corresponding to a molar ratio of 5:1 (fusel alcohols to oil), enzyme loading of 2% w/w, reaction temperature of 35 °C, shaking speed of 250 rpm, and reaction time of 24 h achieved >97% conversion to FAFE. Further, FAFE obtained from reacting a fusel alcohol mixture with corn oil were evaluated for use as a fuel for diesel engines. FAFE mixtures showed superior combustion and cold-flow properties, with the derived cetane numbers up to 4.8 points higher, cloud points up to -6 °C lower, and the heat of combustion up to 2.1% higher than the corresponding FAME samples, depending on the fusel mixture used. This represents a significant improvement for all three metrics, which are typically anti-correlated. Finally, FAFE provides a new opportunity for expanded usage of biodiesel by addressing feedstock limitations, fuel performance, and low temperature tolerance.

09 BIOMASS FUELS↗

DOE OSTI2024

Combustion characteristics of diisopropoxymethane, a low-reactivity oxymethylene ether

Oxymethylene ethers (OMEs) have been studied for use as low-sooting diesel fuel additives or substitutes; very little literature discusses OMEs as spark-ignition (SI) fuels due to their typically high cetane numbers. In this work, a lower-reactivity, branched OME, diisopropoxymethane (DIPM), is evaluated to determine its effectiveness as a spark-ignition fuel, as it is the lowest-reactivity OME (as determined by Indicated Cetane Number) thus far evaluated in the literature. DIPM is synthesized in-house via acetalization from isopropanol (iPrOH) and trioxane using standard OME production practices. DIPM was then tested in a rapid compression machine (RCM) for autoignition and spark ignition characteristics, and in a modified CFR engine to determine effective octane numbers. In the RCM, an autoignition temperature sweep was performed at stoichiometric conditions from 1000/T = 1.7 - 1.0, at 5:1 inert ratio (comparable to approximately 25% EGR), where it was found that DIPM has ignition delay times 5–10x faster than isooctane and displays NTC ignition behavior. Blends with iPrOH indicate that reactivity can be matched with isooctane with low blend ratios of iPrOH in DIPM. Flame speeds were tested with a laser spark for ignition in the RCM, where the flame speed of DIPM and isooctane is determined to be comparable at engine relevant conditions. In the CFR engine, effective RON and MON based on pressure trace frequency domain measurements were determined for DIPM and a 15 vol% iPrOH in DIPM blend. Neat DIPM has (R+M)/2 = 59 and a negative sensitivity of S = -18, consistent with its NTC behavior and higher reactivity. Furthermore the DIPM/iPrOH blend has positive sensitivity and a pump-gasoline range (R+M)/2 = 89.3. DIPM on its own is unlikely to be an effective SI fuel, however, when blended with iPrOH as an ON booster, it may be a promising SI candidate fuel.

09 BIOMASS FUELS↗

DOE OSTI2024

Computational analysis of flame initiation, quenching, and re-ignition in a prechamber natural gas engine under varying EGR-dilution levels

The on-road natural-gas (NG) fueled transportation relies on stoichiometric spark-ignition engines for the advantages of simple after-treatment system despite the efficiency penalty relative to lean-burn combustion strategies. Exhaust gas recirculation (EGR) has the potential to reduce this efficiency gap at low to moderate loads without the need for complex lean-exhaust aftertreatment systems. However, EGR dilution leads to reduced combustion stability and increased cycle-to-cycle variability. A promising technology that has the potential to achieve reliable operation under diluted conditions is the prechamber ignition (or turbulent jet ignition) which uses chemically active turbulent jets generated from combustion inside a prechamber to initiate, stabilize and accelerate combustion of the mixture inside the main chamber. The present work focusses on developing a RANS-based CFD approach to accurately reproduce in-cylinder phenomena in a stoichiometric NG prechamber-assisted heavy-duty engine without relying on complex combustion models that account for turbulence-chemistry interactions. This is necessary because reactive prechamber jets at high EGR dilution tend to extinguish while emerging into the main chamber, which is followed by a phase of re-ignition — a phenomenon that conventional G-equation or well-stirred reactor combustion models cannot reproduce. With addition of a damping multiplier to the well-stirred reactor model, the predictions are seen to show good agreement with experimental pressure evolution and combustion images acquired from a single cylinder Cummins N-14 optical diesel engine retrofitted with a prechamber ignition system. Model predictions of local heat release in the flame and temperature evolution inside the flame are used to investigate combustion dynamics in the prechamber and the main chamber. It is seen that the well-stirred reactor model with the inclusion of damping is able to reproduce the temporary reduction in heat release within the flame, which can be considered equivalent to quenching of jets, and the subsequent re-ignition of the flame inside the main chamber. The delay between quenching and re-ignition depends on the amount of dilution, as explained by an illustration of flame evolution in a Borghi diagram.

Prechamber ignition↗

DOE OSTI2026

Examining experimental nitrogen-based emissions trends from ammonia/diesel and ammonia/hydrogen/diesel combustion

Ammonia has garnered interest as an alternative fuel for power sectors with heavy payload and distance requirements, such as shipping. In this study, ammonia was used in a dual-fuel compression-ignition combustion strategy to overcome some of its technical barriers, using a diesel pilot to ignite a premixed mixture of ammonia and air. Mixtures of premixed ammonia and hydrogen were also explored to evaluate whether the inclusion of hydrogen improves nitrogen-based emissions from the combustion process. A single-cylinder version of a Cummins ISB 6.7 L engine platform was used to experimentally study these effects at various global air/fuel ratios and hydrogen energy fractions. Hydrogen inclusion produced pronounced NOx and N2O emissions, while inclusion of trapped residuals increased N2O but reduced NOx. The two most recent and relevant mechanisms available in the literature—those from Xu and Zhang-Ren-Kokjohn—were used in a chemical kinetics analysis to examine the observed differences in the NOx trends from two dual-fuel ammonia/diesel datasets: (1) in which a portion of the premixed ammonia was substituted with hydrogen and (2) in which the effect of hot trapped residuals was evaluated with only ammonia/air premixed mixtures. The analysis with both mechanisms showed agreement with experimental trends; however, contributions from thermal vs. fuel-borne NOx pathways showed disagreement. A reaction pathway analysis showed that the HNO to NO pathway was the key to NO formation in the mixture.

Tyrewala, Daanish [ORNL] (ORCID:0000000208599324)↗

DOE OSTI2025

Palm oil deoxygenation with glycerol as a hydrogen donor for renewable fuel production using nickel-molybdenum catalysts: The effect of support

Palm oil, one of the most widely used vegetable oils, offers significant potential as a sustainable feedstock for biofuel production. This study explores the deoxygenation of palm oil using glycerol as a hydrogen donor, with nickel-molybdenum (NiMo) catalysts supported on commercial alumina (Al 2 O 3 ), and zeolite (HZSM-5) comparing with self-prepared zirconia (ZrO 2 ). The catalysts were synthesized via incipient wetness impregnation and evaluated for their performance in biofuel production. NiMo/Al 2 O 3 exhibited the lowest oxygen removal efficiency (68.5 %), while NiMo/HZSM-5 achieved a higher oxygen removal (74.3 %) but also demonstrated the highest coke formation. The type of support material influenced the resulting biofuel range, with NiMo/HZSM-5 and NiMo/ZrO 2 favoring jet fuel production, whereas NiMo/Al 2 O 3 was more suitable for diesel production. Notably, NiMo/ZrO 2 exhibited the highest performance in palm oil deoxygenation while minimizing coke formation. These findings highlight NiMo/ZrO 2 as a promising catalyst for efficient and stable biofuel production, with the support material significantly influencing product yield and fuel quality.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Calibrating Synchronous-Generator-Interfaced DG Models in Microgrids Using Multiple Event Data

Microgrids, consisting of distributed generators (DGs), loads, and energy storage systems, play an important role in future smart grids. In order to evaluate how microgrids operate and their dynamic response, it is necessary to develop accurate dynamic models of DGs in microgrids. This paper proposes a systematic method to calibrate synchronous-generator-interfaced DG models. Challenges associated with model validation in microgrids are analyzed. Underlying parameters are categorized into two groups, i.e., the steady-state parameters and time constants, which are estimated in two successive stages using multiple event data. This methodology ensures that the validated model with the estimated parameters are applicable for all events. A modified unscented Kalman filter (UKF) is proposed to deal with the cases in which measurement of power angle is unavailable. The effectiveness of the proposed method is validated using field test data of a 2.6 MVA diesel generator in a real microgrid.

Wang, Zhiwen↗

DOE OSTI2025

2D CFD of lean premixed hydrogen–air flame quenching under locomotive engine conditions

Hydrogen (H 2 ) is a promising fuel for reducing emissions in heavy-duty internal combustion engines (ICEs), but its low quenching distance increases the risk of flame propagation into narrow crevice regions, such as piston-liner gaps. This work uses detailed CFD simulations with finite-rate chemistry to investigate premixed H 2 –air flame quenching in a two-dimensional (2D) region consistent with the piston-liner gap of a diesel ICE. Model accuracy was assessed by comparison with experimentally measured quenching distances in an annular stepwise diverging tube (ASDT). A parametric study was conducted to assess the influence of crevice width (0.05–1.18 mm), pressure (50–150 bar), unburned gas temperature (431–573 K), and equivalence ratio ( Φ= 0.3–0.6). Results show that the critical Péclet number for flame survival is no greater than 3.25, consistent with prior literature, even under ultra-lean and high-pressure conditions (Φ ≤ 0.3, > 50 bar). Additionally, flames with Péclet numbers exceeding 6.15 exhibited front wrinkling, suggesting the onset of velocity-driven instabilities and enhanced flame robustness. These findings help define thresholds for flame quenching in confined geometries and support the safe design of H 2 fueled ICEs.

08 HYDROGEN↗

DOE OSTI2026

System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study

Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗

DOE OSTI2022

Hydroxyapatite catalyzed hydrothermal liquefaction transforms food waste from an environmental liability to renewable fuel

Food waste is an abundant and inexpensive resource for the production of renewable fuels. Biocrude yields obtained from hydrothermal liquefaction (HTL) of food waste can be boosted using hydroxyapatite (HAP) as an inexpensive and abundant catalyst. Combining HAP with an inexpensive homogeneous base increased biocrude yield from 14 ± 1 to 37 ± 3%, resulting in the recovery of 49 ± 2% of the energy contained in the food waste feed. Detailed product analysis revealed the importance of fatty-acid oligomerization during biocrude formation, highlighting the role of acid-base catalysts in promoting condensation reactions. Economic and environmental analysis found that the new technology has the potential to reduce US greenhouse gas emissions by 2.6% while producing renewable diesel with a minimum fuel selling price of $1.06/GGE. HAP can play a role in transforming food waste from a liability to a renewable fuel.

09 BIOMASS FUELS↗

DOE OSTI2022

Biorefinery upgrading of herbaceous biomass to renewable hydrocarbon fuels, Part 2: Air pollutant emissions and permitting implications

The development of advanced biofuel production facilities is still at a nascent stage, and the biorefineries could face challenges in obtaining air permits required for their construction and operation because they are novel emission sources. To fill gaps in knowledge regarding potential emissions, we perform a detailed federal regulatory analysis and estimate air pollutant emissions for an advanced biorefinery that produces renewable diesel blendstock (RDB) from lignocellulosic biomass via aerobic respiration documented in Part 1 of the paper. We evaluate 12 design permutations that include two feedstocks, a uniform format blend (UFB) and corn stover; three biorefinery scales, 2,000, 5,200, and 9,100 dry metric tons per day (dmtd) of lignocellulosic biomass feed; and two lignin uses, as either a boiler fuel or for pellet production. We also evaluate 6 additional design permutations by incorporating non-emitting renewable power, which could reduce up to 63% carbon monoxide (CO) and nitrogen oxides (NO x ) each, 21% volatile organic compounds (VOC), and 43% hazardous air pollutants (HAP) as opposed to on-site power production, for biorefineries that produce pellets, using either UFB or corn stover. Our results indicate that all 18 design permutations would be classified as a major source under the Clean Air Act's New Source Review program without additional emission controls. Compared to using lignin as a boiler fuel, diverting lignin for pellet production reduces the emissions of CO up to 88%, NOx up to 73%, sulfur dioxide (SO 2 ) up to 99%, VOCs up to 72%, and HAPs up to 66%. Additionally, we explore control options that could further reduce emissions and assess whether reductions are enough to achieve minor source classification. Insights from our analysis can help biorefinery developers and regulators develop permitting strategies to mitigate investment risks.

09 BIOMASS FUELS↗

DOE OSTI2021

Performance and cost analysis of liquid fuel production from H 2 and CO 2 based on the Fischer-Tropsch process

Electro-fuels can be produced from concentrated sources of carbon dioxide and hydrogen using electricity generated from renewable sources; this process enables energy storage at high volumetric energy density. Among the electro-fuels options, FT (Fischer-Tropsch) fuel is attractive for heavy-duty trucks and non-road transportation applications. This study conducts a techno-economic analysis of FT liquid fuel production from H 2 and CO 2 using a detailed performance analysis. Minimum fuel selling price is estimated for a broad range of H 2 and CO 2 prices and for a range of potential CO 2 credits. The analysis indicates that H 2 price has the largest impact on the minimum selling price of FT fuel. FT fuel production with a CO 2 price of $17.3/metric ton requires an H 2 price of $0.8/kg to be cost-competitive with the pre-tax petroleum diesel price of $3.1/gal in 2050 (before the application of any CO 2 credits). When the H 2 price is $2.0/kg from central water electrolysis (2020 target), the minimum selling price of the FT fuel is $5.4–5.9/gal. A sensitivity analysis shows that future system optimization of FT fuel production could focus on improving the H 2 and CO 2 recycle contributions and FT fuel conversion ratio. The analysis results can be combined with various upstream systems for H 2 and CO 2 production.

08 HYDROGEN↗

DOE OSTI2023

Uncertainty analysis for techno-economic and life-cycle assessment of wet waste hydrothermal liquefaction with centralized upgrading to produce fuel blendstocks

Wet waste hydrothermal liquefaction is a promising technology for producing transportation fuels with much lower greenhouse gases emissions than petroleum-based fuels. However, its techno-economic and life cycle assessment are primarily based on laboratory scale testing data, subject to considerable uncertainties, and even bias, due to knowledge gaps. Here, a preliminary uncertainty analysis of key economic measures was conducted based on the 2019 state-of-technology model for biocrude production. Building on the preliminary analysis, this work presents a comprehensive uncertainty analysis in both economic and environmental measures of the entire supply chain of wet waste hydrothermal liquefaction to fuel blendstocks including biocrude upgrading based on the 2021 state-of-technology model. The analysis includes the most recent developments in hydrothermal liquefaction and biocrude upgrading technologies and Monte Carlo simulation based on an integrated model system including an improved reactor yield model, reduced-order process model, discounted cash flow economic model and simplified life-cycle assessment model. The estimated biocrude yield ranges from 42.2% to 52.4% with a median of 47.3%. The estimated fuel yield ranges from 34.7% to 42.7% with a median of 38.7%. The estimated minimum fuel selling price ranges from $\$ $2.28/gge to $\$ $3.45/gge with a median of $\$ $2.80/gge. Relative to petroleum-derived diesel, the estimated reduction in supply chain greenhouse gas emissions ranges from 73.4% to 81.8% with a median of 77.7%. Compared to the 2019 state-of-technology analysis, a significant improvement in biocrude selectivity and economic measures and reduction in uncertainties were achieved due to the incorporation of additional continuous experimental data sets, technology development and de-risking, and improvement in model accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A feasibility assessment for co-locating and powering offshore aquaculture with wave energy in the United States

Offshore aquaculture and marine renewable energy (energy from waves, tides, currents, and ocean gradients) are two developing ocean-based industries. Aquaculture, an industry that has typically relied on diesel for power, is expected to grow globally, presenting an opportunity to reduce greenhouse gas emissions by switching to renewable sources as it expands. As the industry moves further offshore and is situated in more energetic environments, the prospect to co-locate offshore aquaculture with wave energy increases. To improve understanding of this potential, a feasibility assessment was completed to estimate the energy needs and wave resource required to power offshore finfish aquaculture operations. The study found it is possible to power offshore aquaculture operations entirely with wave energy. A spatial analysis was then performed to assess the suitability of co-locating offshore finfish aquaculture and wave energy off California and Hawaii. Suitable locations were identified offshore of O’ahu, Hawaii, and northern California. Southern California was also assessed, using a lower wave resource, based on study areas evaluated by the National Oceanic and Atmospheric Administration to identify Aquaculture Opportunity Areas, and while limited there are suitable locations that may warrant further evaluation. This study presents an analysis into the potential to pair wave energy with offshore aquaculture, and how various factors can help determine suitable areas for co-location. The analyses developed in this study will support future identification of potential sites for development and decision-making to optimize the success of co-locating wave energy resources and offshore finfish aquaculture.

16 TIDAL AND WAVE POWER↗