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At least 1,027 records · Page 57

Electrification Futures Study: Operational Analysis of U.S. Power Systems with Increased Electrification and Demand-Side Flexibility

This report provides a high-resolution U.S. national-scale power systems assessment of scenarios with widespread electrification using detailed grid simulations. We find that high electrification scenarios envisioned in the Electrification Futures Study can be operated at the hourly level to meet future increased levels of electrified demand and, under the most aggressive cases, with up to 66% of generation from wind and solar technologies. The analysis also examines the value of flexibility from demand by supporting system operability under high electrification and high renewable systems. Demand-side flexibility—especially flexibility from newly electrified loads—is found to enhance the operational efficiency by reducing VRE curtailment and increasing utilization of generators with lower operating costs. It can also enhance the ability of electrification to decarbonize the energy sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Local Data Integration in East Central Florida

The Applied Meteorology Unit has configured a Local Data Integration System (LDIS) for east central Florida which assimilates in-situ and remotely-sensed observational data into a series of high-resolution gridded analyses. The ultimate goal for running LDIS is to generate products that may enhance weather nowcasts and short-range (less than 6 h) forecasts issued in support of the 45th Weather Squadron (45 WS), Spaceflight Meteorology Group (SMG), and the Melbourne National Weather Service (NWS MLB) operational requirements. LDIS has the potential to provide added value for nowcasts and short-ten-n forecasts for two reasons. First, it incorporates all data operationally available in east central Florida. Second, it is run at finer spatial and temporal resolutions than current national-scale operational models such as the Rapid Update Cycle and Eta models. LDIS combines all available data to produce grid analyses of primary variables (wind, temperature, etc.) at specified temporal and spatial resolutions. These analyses of primary variables can be used to compute diagnostic quantities such as vorticity and divergence. This paper demonstrates the utility of LDIS over east central Florida for a warm season case study. The evolution of a significant thunderstorm outflow boundary is depicted through horizontal and vertical cross section plots of wind speed, divergence, and circulation. In combination with a suitable visualization too], LDIS may provide users with a more complete and comprehensive understanding of evolving mesoscale weather than could be developed by individually examining the disparate data sets over the same area and time.

Case, Jonathan L.↗

Assessing geothermal/solar hybridization – Integrating a solar thermal topping cycle into a geothermal bottoming cycle with energy storage

Geothermal and concentrating solar power (CSP) technologies typically use heat at different temperatures in their commercial deployments. This enables a technically viable hybridization of a solar topping cycle and a geothermal bottoming cycle at locations where both resources are available. In this article, an underperforming geothermal power system based at Burley, Idaho is used as a baseline to investigate the technical and economic potential of such a hybrid cycle. A direct thermal energy storage (TES) system is also integrated to overcome the intermittency of the solar resource. Design and off-design behavior of key components are modelled to simulate the annual performance of the hybrid system on an hourly basis. The sizing of the solar field and thermal storage is investigated and is seen to significantly impact the annual electricity generation and efficiency. Various cost scenarios for the solar field and thermal energy storage are investigated. An economic metric - levelized cost of electricity (LCOE) - is used to optimize the solar field sizing and TES capacity. The hybrid plant converts the additional solar heat input into additional work with an efficiency of 32.9%. Retrofitting a geothermal plant with solar and eight hours of energy storage can achieve an LCOE of 0.136 $/kWhe in current cost scenarios and 0.081 $/kWhe in a future cost reduction scenario. Although the hybrid system cannot directly compete with current PV systems without batteries, it has an LCOE 32% lower than that of a PV-battery system. This paper provides insights into the research and development of the future grid with a high renewable energy penetration and encourages further study of energy hybridization for improved efficiencies and economics.

15 GEOTHERMAL ENERGY↗

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

Analysis of Transformer Magnetizing Reactance Using Describing Function in Direct Power Control of Back-to-Back Modular Multilevel Converter with Advanced Grid Support

This paper presents a Port Controlled Hamiltonian based direct power control architecture for a back-to-back modular multilevel converter system connecting two ac sources at different frequencies. The system features advanced grid support functionalities based on IEEE 1547-2018, implemented on the inverter side. The rectifier side controller ensures reference following for active and/or reactive powers and maintains the commanded dc bus voltage. The inverter side controller ensures power command following for active and reactive powers.The proposed control architecture is designed to suppress second harmonic oscillations in powers during unbalanced grid voltage sags by dynamically adjusting the currents on each ac side. This also ensures effective elimination of any second harmonic oscillations in the equivalent dc bus voltage. Validation is performed on an OPAL-RT real-time platform with case studies on unbalanced and balanced sags, demonstrating the controller's effectiveness during real time implementation. A reduced-scale laboratory prototype further verifies these case studies, with experimental results for balanced sags due to grid simulator limitations. The results confirm the robustness and efficiency of the proposed control strategy in ensuring stable and reliable operation under various grid conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

The Fluid Dynamics Uncertainty Quantification Challenge Problem: XFOIL vs. MFOIL

Uncertainty quantification (UQ) has become more critical in aerospace engineering due to the growing dependence on computational tools for design optimization and performance analyses of aerospace vehicles. Even though the significance of UQ in assessing the credibility of computational analyses is well recognized, its costs and complexity impede its integration into standard practices, particularly in computational fluid dynamics (CFD) and other fluid analyses. This paper presents a UQ study for low-fidelity computational aerodynamics analyses with XFOIL and mfoil (i.e., the MATLAB version of XFOIL with several implementation modifications); these tools are utilized widely in both research and education. The main contributions of this paper are as follows: 1) improved precision in quantifying the uncertainty of the baseline Monte Carlo results used to benchmark surrogate modeling techniques for UQ, 2) quantification of the effect of the implementation differences between XFOIL and mfoil on solution quantities of interest (QoIs), such as lift and pitching moment coefficients, and 3) development of an open-source UQ library for use with XFOIL and mfoil, which has educational values and helps promote UQ for fluid analyses with aerospace applications. Results and discussions revolve around cases 1-4 of the challenge problem posed by the AIAA Fluid Dynamics Technical Committee’s Uncertainty Quantification Discussion Group (UQDG). In case 3, this work employs CFDverify, an open-source solution verification software, to quantify the discretization error and evaluate the extrapolated QoIs based on the grid convergence index (GCI). This UQ study differentiates itself from previous studies in the rigor of handling baseline Monte Carlo uncertainty and in including mfoil, which is a more accessible alternative to XFOIL. Finally, despite the growing computing power, low-fidelity computational tools remain valuable, such as for aerodynamic shape optimization at Mach numbers below 0.65 and low-to-mid Reynolds numbers.

Lay, Aidan S [University of Tennessee, Knoxville (↗

Microgrid Resilience: A Holistic and Context-Aware Resilience Metric

Microgrids present an effective solution for the coordinated deployment of various distributed energy resources and furthermore provide myriad additional benefits such as resilience, decreased carbon footprint, and reliability to energy consumers and the energy system as a whole. Boosting the resilience of distribution systems is another major benefit of microgrids. This is because they can also serve as a backup power source when the utility grid's operations are interrupted due to either high-probability low-impact events like a component failure or low-probability high-impact events - be it a natural disaster or a planned cyberattack. However, the degree to which any particular system can defend, adapt, and restore normal operation depends on various factors including the type and severity of events to which a microgrid is subjected. These factors, in turn, are dependent on the geographical location of the deployed microgrid as well as the cyber risk profile of the site where the microgrid is operating. Therefore, in this work, we attempt to capture this multi-dimensional interplay of various factors in quantifying the ability of the microgrid to be resilient in these varying aspects. This paper, thus, proposes a customized site-specific quantification of the resilience strength for the individual microgrid's capability to absorb, restore, and adapt to the changing circumstances for sustaining the critical load when a low-probability high-impact event occurs - termed as - context-aware resilience metric. We also present a case study to illustrate the key elements of our integrated analytical approach.

microgrid↗

An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Preliminary Process and Instrumentation Design of Advanced Reactor Integration with Refineries and Hydrogen Production Facilities

With the ongoing push to decarbonize energy use and especially greenhouse gas emissions across all sectors, there are incentives to investigate how nuclear reactors may be used to generate clean energy and be used in various energy economies beyond just the electrical grid. Two initial integrations, high temperature steam electrolysis (HTSE) and oil refineries, are investigated in this first DOE Integrated Energy Systems (IES) program detailed industrial integration design report. Increasingly detailed reports are anticipated both for the industries discussed in this report and for additional industries in the future of the program. This report is a robust starting point showing how integration thermodynamic analysis establishes the requirements on the reactor and methods by which those requirements can be evaluated for specific reactor designs. Two Advanced Reactor Demonstration Program awardees are selected as representative designs for their respective technologies: NuScale for light-water reactors (LWRs) and X-Energy for high temperature gas reactors (HTGRs). Other reactor technologies or specific reactor configurations would require specific analysis similar to what is done in this report, thus this report can be a reference point by which to extend this work to other nuclear plant designs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

Feeding Ten Billion People Is Possible Within Four Terrestrial Planetary Boundaries

Global agriculture puts heavy pressure on planetary boundaries, posing the challenge to achieve future food security without compromising Earth system resilience. On the basis of process-detailed, spatially explicit representation of four interlinked planetary boundaries (biosphere integrity, land-system change, freshwater use, nitrogen flows) and agricultural systems in an internally consistent model framework, we here show that almost half of current global food production depends on planetary boundary transgressions. Hotspot regions, mainly in Asia, even face simultaneous transgression of multiple underlying local boundaries. If these boundaries were strictly respected, the present food system could provide a balanced diet (2,355 kcal per capita per day) for 3.4 billion people only. However, as we also demonstrate, transformation towards more sustainable production and consumption patterns could support 10.2 billion people within the planetary boundaries analysed. Key prerequisites are spatially redistributed cropland, improved water–nutrient management, food waste reduction and dietary changes. Adoption of the Sustainable Development Goals by all nations in 2015 is the first ever commitment to a world development path that safeguards the stability of the Earth system as a prerequisite for meeting universal human standards1. The longstanding challenge of achieving food security through sustainable agriculture is particularly acute in this context as world agriculture is a leading cause for the current transgressions of multiple planetary boundaries (PBs) globally and regionally2–5. The PB framework is a comprehensive scientific attempt to synoptically define our planet’s biogeophysical limits to anthropogenic interference. It suggests bounds to nine interacting processes that together delineate a Holocene-like Earth system state. The Holocene is chosen as the reference state as it is the only period known to provide a safe operating space for a world population of several billion people, and according to a precautionary principle, the PBs are set in sufficient distance from processes that may critically undermine Earth system resilience and global sustainability. A challenging question, thus, is whether human development goals such as food security can be met while maintaining multiple PBs along with their subglobal manifestations. Further PB transgressions could jeopardize the chances of providing sufficient food for a world population projected to be wealthier and reach >9 billion by 2050. This conundrum portrays a tradeoff between Earth’s biophysical carrying capacity and humankind’s rising food demand, calling in response for radical rethinking of food production and consumption patterns6–9. Yield gap closures, avoidance of excessive input use, shifts towards less resource-demanding diets, food waste reductions and efficient international trade are crucial options for sustainably increasing the food supply10–15. For example, enhancing water-use efficiency on irrigated and rain-fed farms can triple or quadruple crop yields in low-performing systems, suggesting possible global gains of >20% (ref. 16). Even higher gains appear feasible through globally optimized configurations of the land-use pattern17, and cutting food losses by half could generate food for another billion people18. Thus, collective large-scale implementation of such options could sustain food for a further growing world population19. Yet achieving this within a safe operating space as defined by PBs requires not only a halt to but actually a reversal of existing PB transgressions. Previous studies suggest that such a reconciliation might be possible, but these were based on aggregate representations of PBs (not accounting for the spatial patterns of limits, transgressions and interactions) or considered only one boundary in isolation17,20–23. Here, we systematically quantify to what extent current food production depends on local to global transgressions of the PBs for biosphere integrity, land-system change, freshwater use and nitrogen (N) flows, along with the potential of a range of solutions to avoid these transgressions and still increase food supply (Table 1). To this end, we configured an internally consistent process-based model of the terrestrial biosphere including agriculture (LPJmL) with multiple spatially distributed PBs and their interactions. LPJmL is among the longest-established and best-evaluated biosphere models, showing robust performance regarding simulation of, for example, carbon, water and crop yield dynamics (Supplementary Figs. 1 and 2 and Supplementary Table 1; see ref. 24 for a comprehensive benchmarking and Supplementary Methods for more detail on model evaluations). In principle following established definitions4, we refine the computation of some PBs with respect to their regional patterns and interactions (Methods), providing globally gridded precautionary limits to human interference with the Earth system at a level of great detail. In particular, we account for the evidence that many PBs need to be represented spatially explicitly4 to cover their

Gerten, Dieter↗

Predicting Geologic Behavior in Carbon Storage Projects Using Graph Neural Network

This study was invited to presented at NVIDIA's GTC conference to highlight the potential of Graph Neural Network as a novel and promising methodology for predicting pressure and saturation evolution in carbon storage projects. Carbon capture and storage (CCS) technology plays a pivotal role in mitigating greenhouse gas emissions, facilitating the transition to a low-carbon future. Effective management of subsurface reservoirs is essential to ensure the safe and efficient storage of captured carbon dioxide (CO₂). Accurate predictions of pressure and saturation over time are critical for evaluating the long-term performance and integrity of CCS projects. In recent years, Graph Neural Network (GNN) has emerged as a powerful framework for analyzing complex data in graph-structured domains. This abstract explores the application of GNN to forecast pressure and saturation evolution in carbon storage projects. Traditional numerical simulations of subsurface reservoirs have proven successful in providing pressure and saturation forecasts. However, these simulations involve massive amounts of computational effort and require extensive domain expertise for proper model calibration and validation. Graph Neural Operator offers an alternative approach that harnesses the inherent graph structure of reservoirs, where nodes represent reservoir grid cells and edges represent the geological connectivity between them.

Shih, Chung Yan↗

The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure; A Nationwide Assessment

Ambitious federal clean goals, along with historic investment in American manufacturing, have put the United States on track to see 30-42 million light-duty electric vehicles (EVs) on the road by 2030. Now, a groundbreaking study from the National Renewable Energy Laboratory (NREL) has estimated the EV charging infrastructure needed nationwide to support a sweeping transition to electrified transportation. The study, titled "The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure," estimates the number, type, and location of the chargers needed to create a comprehensive network of EV charging infrastructure. Its use of proprietary NREL software tools and sophisticated analysis have resulted in a nationwide infrastructure needs assessment with a never-before-seen level of detail - one that takes into account the different ways Americans travel, from running errands to taking road trips, and can adjust to changing circumstances as EV adoption rates change over time.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

Opportunities for Clean Energy in Natural Gas Well Operations

The oil and gas industry is increasingly seeking operational improvements to reduce both costs and emissions while improving resilience against electric grid outages. This study describes techno-economic analysis of opportunities for distributed energy generation and storage technologies to support companies’ energy cost savings, clean energy, and energy resiliency goals. Specifically, the analysis evaluates solar photovoltaics (PV), distributed wind energy, and battery energy storage at hypothetical upstream well sites in the Marcellus Shale in Pennsylvania, both grid-connected and off-grid. Results indicate opportunity for solar PV to reduce operational costs. Additionally, these technologies reduce the site’s consumption of grid electricity and natural gas and thus can help reduce Scope 1 and 2 emissions associated with electricity and natural gas consumption. For each emissions reduction scenario, a cost of avoided emissions was calculated; these values can be compared to internal organizational value placed on emissions reductions, compared to other emissions reduction strategies such as energy efficiency, reducing flaring, and direct carbon capture and sequestration, and compared to existing (albeit limited) U.S. carbon markets such as California’s Low Carbon Fuel Standard. Results indicate that the associated costs of emissions reductions via distributed renewables are competitive with these options and markets. The study also explores the ability of these electric clean energy technologies to support site resiliency against utility outages.

42 ENGINEERING↗

Opportunities for Clean Energy in Natural Gas Well Operations

The oil and gas industry is increasingly seeking operational improvements to reduce both costs and emissions while improving resilience against electric grid outages. This study describes techno-economic analysis of opportunities for distributed energy generation and storage technologies to support companies' energy cost savings, clean energy, and energy resiliency goals. Specifically, the analysis evaluates solar photovoltaics (PV), distributed wind energy, and battery energy storage at hypothetical upstream well sites in the Marcellus Shale in Pennsylvania, both grid-connected and off-grid. Results indicate opportunity for solar PV to reduce operational costs. Additionally, these technologies reduce the site's consumption of grid electricity and natural gas and thus can help reduce Scope 1 and 2 emissions associated with electricity and natural gas consumption. For each emissions reduction scenario, a cost of avoided emissions was calculated; these values can be compared to internal organizational value placed on emissions reductions, compared to other emissions reduction strategies such as energy efficiency, reducing flaring, and direct carbon capture and sequestration, and compared to existing (albeit limited) U.S. carbon markets such as California's Low Carbon Fuel Standard. Results indicate that the associated costs of emissions reductions via distributed renewables are competitive with these options and markets. The study also explores the ability of these electric clean energy technologies to support site resiliency against utility outages.

42 ENGINEERING↗