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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Functional composites by programming entropy-driven nanosheet growth

Here, nanomaterials must be systematically designed to be technologically viable. Driven by optimizing intermolecular interactions, current designs are too rigid to plug in new chemical functionalities and cannot mitigate condition differences during integration. Despite extensive optimization of building blocks and treatments, accessing nanostructures with the required feature sizes and chemistries is difficult. Programming their growth across the nano-to-macro hierarchy also remains challenging, if not impossible. To address these limitations, we should shift to entropy-driven assemblies to gain design flexibility, as seen in high-entropy alloys, and program nanomaterial growth to kinetically match target feature sizes to the mobility of the system during processing. In tis report, following a micro-then-nano growth sequence in ternary composite blends composed of block-copolymer-based supramolecules, small molecules and nanoparticles, we successfully fabricate high-performance barrier materials composed of more than 200 stacked nanosheets (125 nm sheet thickness) with a defect density less than 0.056 µm -2 and about 98% efficiency in controlling the defect type. Contrary to common perception, polymer-chain entanglements are advantageous to realize long-range order, accelerate the fabrication process (<30 min) and satisfy specific requirements to advance multilayered film technology. This study showcases the feasibility, necessity and unlimited opportunities to transform laboratory nanoscience into nanotechnology through systems engineering of self-assembly.

42 ENGINEERING↗
DOE OSTI2016

A MILP-Based Distribution Optimal Power Flow Model for Microgrid Operation

This paper proposes a distribution optimal power flow (D-OPF) model for the operation of microgrids. The proposed model minimizes not only the operating cost, including fuel cost, purchasing cost and demand charge, but also several performance indices, including voltage deviation, network power loss and power factor. It co-optimizes the real and reactive power form distributed generators (DGs) and batteries considering their capacity and power factor limits. The D-OPF is formulated as a mixed-integer linear programming (MILP). Numerical simulation results show the effectiveness of the proposed model.

distributed generators↗
DOE OSTI2020

Optimal Equilibrium Selection of Price-Maker Agents in Performance-Based Regulation Market

This paper analyzes the oligopolistic equilibrium of multiple price-maker agents in performance-based regulation (PBR) markets. In these markets, there are price-maker agents representing some frequency regulation (FR) providers and a number of independent price-taker FR providers. An equilibrium problem with equilibrium constraints (EPEC) model is employed in this paper to study the equilibria of a PBR market in the presence of price-maker agents and pricetaker FR providers. Due to the incorporation of the FR providers' dynamics, the proposed model is reformulated as a mixed-integer linear programming (MILP) problem over innovative mathematical techniques. An optimal equilibrium point is also selected for the market, where none of the agents is unique deviator and the dynamic performance of power system is improved simultaneously. The effectiveness of proposed optimal equilibrium point is evaluated in the numerical results section by comparing the outputs with conventional optimal dispatches of the FR providers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Calcium Phosphate Constraints in HLW Glasses: Phosphate Liquidus Model

About 50 million gallons of high-level mixed waste is currently stored in underground tanks at the United States Department of Energy’s (DOE’s) Hanford site in the State of Washington. The Hanford Tank Waste Treatment and Immobilization Plant (WTP) will provide DOE’s Office of River Protection (ORP) with a means of treating this waste by vitrification for subsequent disposal. The tank waste will be separated into low- and high-activity waste fractions, which will then be vitrified respectively into Immobilized Low Activity Waste (ILAW) and Immobilized High Level Waste (IHLW) products. The ILAW product will be disposed in an engineered facility on the Hanford site while the IHLW product is designed for acceptance into a national geological disposal facility for high-level nuclear waste. The ILAW and IHLW products must meet a variety of requirements before they can be accepted for disposal. Glass property-composition models will be used to control the vitrification process at the WTP to ensure that the glass meets product quality and processability requirements. The quality and the validity ranges of the property-composition models and the supporting database will greatly influence waste treatment operations, including the waste loading that is achievable in the glass for each waste composition. The current baseline models for operation of the WTP were developed under the Bechtel National, Inc. (BNI) WTP contract. These models were based on LAW and HLW glass property data over a range of compositions that was guided primarily by the WTP contract and the set of tanks specified therein. The HLW testing was directed towards waste streams from the then-planned early feed tanks for the WTP (i.e., AZ-101, AZ-102, C-106/AY-102, and C-104/AY-101). These wastes are high in iron (AZ-101, AZ-102, and C-106/AY-102) or thorium (C-104/AY-101). While the baseline models may be adequate for commissioning of the facility and processing of the specified tanks, refinements and extensions or augmentations of these models will be required to process the balance of the tank waste inventory. The overall mission of the WTP will require the immobilization of tank waste compositions that are variously dominated by mixtures of aluminum, chromium, bismuth, iron, phosphorous, zirconium, and sulfur compounds as waste-loading-limiting components. Further, the WTP contract includes minimum waste loading requirements that are generally rather modest while loadings significantly higher than those in the current baseline are likely achievable. In order to achieve these higher waste loadings and to expand the applicable composition regions, new models will be needed to support the optimized operation of the WTP. Since the development of the baseline WTP models, ORP directed a program of work at the Vitreous State Laboratory (VSL) to examine various approaches for optimization of the performance of the WTP, including increasing the waste loadings in the LAW and HLW glass products. That work involved HLW rich in Al, Al+Na, Cr, Bi, S, Zr, Sr, P and Fe, in contrast to old baseline models that were based mostly on HLW tanks that were high in Fe, with one other that was also high in Th. Glass property data collected under the ORP program were compiled and combined with data collected to develop the WTP models. The combined databases have been used to update pre-existing HLW composition models or to develop new models for properties such as Product Consistency Test (PCT) responses, spinel crystallization (one-percent crystal fraction temperature, T 1% ), melt viscosity, melt electrical conductivity, nepheline formation upon slow cooling (i.e., Canister Centerline Cooling [CCC]), and Toxicity Characteristic Leach Procedure (TCLP) Cd response. While spinel is typically the most commonly occurring phase in HLW glasses, other phases can be limiting depending on the waste and glass composition. For example, various phosphate phases can become important as the phosphorus content increases. Lithium phosphate crystallization can impact PCT releases of HLW glasses, while calcium phosphate phases forming in the cold-cap can deleteriously impact feed processing in the melter. At present, however, no models exist that relate glass composition and crystallization of phosphate phases in general and calcium phosphate in particular. The present work was therefore directed towards the initial development of such models. Future work may be directed at studying amorphous phase separation in high phosphate HLW glasses. Within the River Protection Project, feed compositions are projected using the Hanford Tank Waste Operations Simulator (HTWOS), a dynamic computer model that tracks the waste through storage, retrieval, feed staging, and treatment processes. A review of the HTWOS data shows that WTP will process 4106 batches of HLW glass for the overall mission (System Plan Rev. 6, ORP-11242), each with its own waste feed and glass composition. Some of the projected HTWOS feed batches are expected to have relatively high concentrations of phosphorous compared to typical HLW streams. In the 4106 batches of HLW for the overall WTP mission P 2 O 5 varies from 0.59 wt% to 13.00 wt%, with 95 batches containing at least 10 wt% P 2 O 5 . Occurrences of phosphate crystals have commonly been noted in glasses with high bismuth and phosphorus contents. In addition, recently collected data from a set of statistically designed HLW matrix glasses prepared and characterized to support enhancement of the HLW glass property-composition models identified apatite in a number of glass samples. However, the current HLW model for crystallization (one-percent crystal fraction temperature) is applicable only to spinel crystallization in glasses. Previous projections of waste loadings and glass volume (such as those in System Plan Rev. 6, ORP-11242) have used preliminary constraints for calcium phosphate crystallization that are based on the limited available data. Additional data and improved constraints or, preferably, models that relate glass composition to crystallization of phosphate-containing phases, are therefore needed to support HLW vitrification at the WTP. The present study focuses on the development of such a model based on data from previously characterized glasses and a set of newly fabricated glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Building Load Control Using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants

Aggregation of heating, ventilation, and air conditioning (HVAC) loads can provide reserves to absorb volatile renewable energy, especially solar photo-voltaic (PV) generation. In this paper, we decide HVAC control schedules under uncertain PV generation, using a distributionally robust chance-constrained (DRCC) building load control model under two typical ambiguity sets: the moment-based and Wasserstein ambiguity sets. We derive mixed integer linear programming (MILP) reformulations for DRCC problems under both sets. Especially, for the Wasserstein ambiguity set, we use the right-hand side (RHS) uncertainty to derive a more compact MILP reformulation than the commonly known MILP reformulations with big-M constants. All the results also apply to general individual chance constraints with RHS uncertainty. Furthermore, we propose an adjustable chance-constrained variant to achieve tradeoff between the operational risk and costs. We derive MILP reformulations under the Wasserstein ambiguity set and second-order conic programming (SOCP) reformulations under the moment-based set. Using real-world data, we conduct computational studies to demonstrate the efficiency of the solution approaches and the effectiveness of the solutions. Summary of Contribution: The problem studied in this paper is motivated by a building load control problem that uses the aggregation of heating, ventilation, and air conditioning (HVAC) loads as flexible reserves to absorb uncertain solar photovoltaic (PV) generation. The problem is formulated as distributionally robust chance-constrained (DRCC) programs with right-hand side (RHS) uncertainty. In addition, we propose a risk-adjustable variant of the DRCC programs, where the risk level, instead of being predetermined, is treated as a decision variable. The paper aims to provide tractable reformulations and solution algorithms for both the (general) DRCC and the (general) adjustable DRCC models with RHS uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Gaussian Process Optimization of Sensitivity-Based Similarity Metrics between New Nuclear Applications and New/Existing Benchmarks [Slides]

This presentation discusses Nuclear Criticality Safety (NCS) and how designing safe, new nuclear criticality experiments requires expert judgement, which could take years of experience. Sensitivity/uncertainty (S/U) analysis can be utilized by less experienced individuals to conservatively estimate uncertainties in important parameters, such as k eff , in newly proposed nuclear experiments. The presentation poses the question of how this analysis can be performed and states that the answer lies in matching new nuclear experiments with existing benchmark experiments using similarity metrics. By increasing the criticality safety of the application in this work, higher mass limits could be used in PF-4 operations. Additionally, the presentation discusses MCNP6.2®, Whisper-1.1, the software that can be used in this analysis. Also discussed is the fact that International Criticality Safety Benchmark Evaluation Project (ICSBEP) benchmarks rarely match new nuclear applications and that there are significant differences in given set of materials and/or geometry. If there are no benchmarks that match the application, the presentation discusses the possibility of creating new benchmarks. In conclusion, this work presents a Gaussian process (GP) optimization scheme that was used to generate new benchmarks with the highest sensitivity-based similarity metrics to user-defined nuclear applications. The Gaussian process optimization successfully designed 3 new experimental benchmarks that were highly correlated to the application of interest and had k eff values near critical. Optimization over c k,i-r has shown that investigating specific isotope reactions for different applications is crucial to designing benchmark experiments. Partial contribution from Pu dominates c k similarity metric. Future work includes testing new stand-alone similarity metrics or new combinations of similarity metrics as the design criterion of this optimization – design criterion is application dependent.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Hybrid Particle Swarm-Interpolation Algorithm for Improved Critical Experiment Design

The typical goal of designing a critical experiment is twofold: a system that is both critical and optimized for some other value. This value could be an energy-integrated sensitivity, percent fissions in a certain energy range, or some other value that can be calculated by a transport code. By simulating different combinations of reflector, moderator, and fuel thicknesses a designer can identify such a desirable configuration. The domain of all possible combinations of these thicknesses is referred to as the experiment search space. As more dimensions are added, the search space increases in size exponentially. For a three-dimensional case, which includes three thickness values between zero and ten centimeters to the nearest tenth of a millimeter, a total of 1,000 3 , configurations exists. Rather than check each configuration individually, which would be extremely computationally expensive, it has been shown to be more efficient to use a conventional particle swarm optimization (PSO) algorithm to identify critical and optimal configurations. This work presents the theory and implementation of a novel hybrid PSO interpolation algorithm to perform these optimizations faster than a conventional PSO algorithm. To demonstrate this, an example optimization will be carried out by the conventional and hybrid PSO algorithms and their performances will be compared.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

MO-DE-207-04: Imaging educational program on solutions to common pediatric imaging challenges

This imaging educational program will focus on solutions to common pediatric imaging challenges. The speakers will present collective knowledge on best practices in pediatric imaging from their experience at dedicated children’s hospitals. The educational program will begin with a detailed discussion of the optimal configuration of fluoroscopes for general pediatric procedures. Following this introduction will be a focused discussion on the utility of Dual Energy CT for imaging children. The third lecture will address the substantial challenge of obtaining consistent image post -processing in pediatric digital radiography. The fourth and final lecture will address best practices in pediatric MRI including a discussion of ancillary methods to reduce sedation and anesthesia rates. Learning Objectives: To learn techniques for optimizing radiation dose and image quality in pediatric fluoroscopy To become familiar with the unique challenges and applications of Dual Energy CT in pediatric imaging To learn solutions for consistent post-processing quality in pediatric digital radiography To understand the key components of an effective MRI safety and quality program for the pediatric practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Piecewise Linear Optimization for Public EV Charging Depots in Different Utility Environments

With the proliferation of electric vehicles (EVs), EV supply equipment technology is being pushed to increasingly higher power levels, such as extreme fast charging (XFC) at 300kW or more. As more public XFC depots are connected to electric distribution systems, utility operators will mitigate their impact with pricing schemes and programs such as demand response, demand charges, and time-varying prices. In this paper we describe an XFC management system that combines an on-site battery energy storage system and an optimization application that minimizes the cost to operate the depot in light of the utility pricing programs. We have designed the optimizer specially to use fast and simple linear programming so that it can be deployed to low-cost computing platforms. We also describe how several utility pricing schemes are integrated into a single piecewise model, reducing the engineering effort to implement the optimizer in different utility environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

POINT: Partially Observable Imitation Network for Traffic Signal Control

Smart traffic signals bring together transportation infrastructure and advance technologies to improve the mobility and efficiency of urban transportation network. Adaptive traffic signal control studies can be categorized into modeling-based approaches and learning-based approaches. In order to take advantages of these two systems, this study developed an offline-online combined Partial Observable Imitation Network for Traffic signal control (POINT). In the offline system, the traffic signal timing optimization problem was formulated as a Mixed Integer Nonlinear Programming (MINLP) given complete traffic information, i.e., second-by-second speeds and locations of all vehicles. Furthermore, the objective of MINLP is to minimize total travel delays considering individual vehicle trajectories under Connected Vehicle (CV) environment. The calculated optimal solutions under various traffic conditions were considered as the ”expert” decisions. In the online system, an imitation neural network model was developed to learn the ”expert” signal plans generated from offline system. Given partial observable traffic conditions in real time, e.g., the aggregate-level of traffic volume, the POINT model can compute the signal timing parameters in the online system. The numerical results demonstrated that the proposed method outperformed other state-of-the-art signal control method under high and unbalanced traffic demand levels in terms of reducing travel delays and queue length.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Optimal Power Flow in DC Networks with Robust Feasibility and Stability Guarantees

With high penetrations of renewable generation and variable loads, there is significant uncertainty associated with power flows in DC networks such that stability and operational constraint satisfaction are of concern. Most existing DC network optimal power flow (DN-OPF) formulations assume exact knowledge of loading conditions and do not provide stability guarantees. Here, in contrast, this paper studies a DN-OPF formulation which considers both stability and operational constraint satisfaction under uncertainty. The need to account for a range of uncertainty realizations in this paper's robust optimization formulation results in a challenging semi-infinite program (SIP). The proposed solution algorithm reformulates this SIP into a computationally tractable problem by constructing a tight convex inner approximation of the stability set using sufficient conditions for the existence of a feasible and stable power flow solution. Optimal generator set-points are obtained by optimizing over the proposed convex stability set. The validity and effectiveness of the propose algorithm is demonstrated through various DC networks adapted from IEEE test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

Numerical study of a matrix-free trust-region SQP method for equality constrained optimization

This is a companion publication to the paper 'A Matrix-Free Trust-Region SQP Algorithm for Equality Constrained Optimization' in which we develop and analyze a trust-region sequential quadratic programming (SQP) method that supports the matrix-free (iterative, in-exact) solution of linear systems. In this report, we document the numerical behavior of the algorithm applied to a variety of equality constrained optimization problems, with constraints given by partial differential equations (PDEs).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Tough Errors are no Match (TEAM): Optimizing the Quantum Compiler for Noise Resilience

This project builds toward a comprehensive error-mitigating toolkit that makes quantum programming more robust and adaptive to the noisy, resource-limited nature of today’s quantum hardware. To that end, it integrates established error-mitigation methods — such as zero-noise extrapolation and dynamical decoupling — directly into compiler infrastructures. These techniques will be packaged as modules that can automatically adjust and combine based on performance analysis, enabling compilers to explore large design spaces and produce optimized, low-noise quantum programs with minimal manual intervention. In parallel, this project also explores new approaches to analog quantum programming or quantum simulation, and has developed the programming language SimuQ which treats quantum Hamiltonian evolution as the central object.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Co-optimized trading of hybrid wind power plant with retired EV batteries in energy and reserve markets under uncertainties

To be competitive in the electricity markets, various technologies have been reported to increase profits of wind farm owners. Combining battery storage system, wind farms can be operated as conventional power plants which promotes the integration of wind power into the power grid. However, high expenses on batteries keep investors away. Retired EV batteries, fortunately, still have enough capacity to be reused and could be obtained at a low price. In this work, a two-stage optimization of a wind energy retired EV battery-storage system is proposed. The economic performance of the proposed system is examined concerning its participation in the frequency containment normal operation reserve (FCR-N) market and the spot market simultaneously. To account uncertainties in the wind farm output, various electricity market prices, and up/down regulation status, a scenario-based stochastic programming method is used. The sizing of the equipment is optimized on top of daily operations of the hybrid system which formulates a mixed-integer linear programming (MILP) problem. Scenarios are generated with the Monte Carlo simulation (MCS) and Roulette Wheel Mechanism (RWM), which are further reduced with the simultaneous backward method (SBM) to increase computational efficiency. A 21 MW wind farm is selected as a case study. Here, the optimization results show that by integrating with a retired EV battery-storage system (RESS) and a bi-directional inverter, the wind farm can increase its profits significantly when forwarding bids in both of the aforementioned electricity markets.

17 WIND ENERGY↗
DOE OSTI2012

Modular Energy Storage System for Alternative Energy Vehicles

An electrical vehicle environment was established to promote research and technology development in the area of high power energy management. The project incorporates a topology that permits parallel development of an alternative energy delivery system and an energy storage system. The objective of the project is to develop technologies, specifically power electronics, energy storage electronics and controls that provide efficient and effective energy management between electrically powered devices in alternative energy vehicles plugin electric vehicles, hybrid vehicles, range extended vehicles, and hydrogen-based fuel cell vehicles. In order to meet the project objectives, the Vehicle Energy Management System (VEMS) was defined and subsystem requirements were obtained. Afterwards, power electronics, energy storage electronics and controls were designed. Finally, these subsystems were built, tested individually, and integrated into an electric vehicle system to evaluate and optimize the subsystems performance. Phase 1 of the program established the fundamental test bed to support development of an electrical environment ideal for fuel cell application and the mitigation of many shortcomings of current fuel cell technology. Phase 2, continued development from Phase 1, focusing on implementing subsystem requirements, design and construction of the energy management subsystem, and the integration of this subsystem into the surrogate electric vehicle. Phase 2 also required the development of an Alternative Energy System (AES) capable of emulating electrical characteristics of fuel cells, battery, gen set, etc. Under the scope of the project, a boost converter that couples the alternate energy delivery system to the energy storage system was developed, constructed and tested. Modeling tools were utilized during the design process to optimize both component and system design. This model driven design process enabled an iterative process to track and evaluate the impact of design alternatives and the impact of changes. Refinement of models was accomplished through correlation studies to measured data obtained from functioning hardware. Specifically, correlation and characterization of the boost converter resulted in a model that was effectively used to determine overall VEMS performance. The successful development of the boost converter can be attributed to utilization of previously proven technologies and adapting to meet the VEMS requirements. This program provided significant improvement in development time of various generations of boost converters. The software strategies and testing results support the development of current energy management systems and directly contribute to the future of similar, commercial products at Magna E-Car Systems. Because of this development project, Magna E-Car Systems is able to offer automotive customers a boost converter system with reduced time to market and decreased product cost, thus transferring the cost and timing benefits to the end use consumer.

25 ENERGY STORAGE↗
DOE OSTI2010

Initial Flowsheet for the Next Generation GSSX Process in the MCU: Report for FY2010

The Next Generation Caustic Side Solvent Extraction (NG-CSSX) flowsheet is being developed in a collaboration among Oak Ridge National Laboratory (ORNL), Argonne National Laboratory (ANL), and Savannah River National Laboratory (SRNL) for the Modular CSSX Unit (MCU) facility at the Savannah River Site (SRS). This work is being done in cooperation with two groups at SRS (my principal contacts there are Mark Geeting, SRR, and Sam Fink, SRNL) and with one group at Oak Ridge National Laboratory (my principal contact there is Bruce Moyer). The evaluation of the various NG-CSSX solvent candidates was done using the SASSE (Spreadsheet Algorithm for Stagewise Solvent Extraction) program with cesium distribution coefficient (D Cs ) values supplied by ORNL. The final optimized solvent composition has 50 mM MaxCalix as the extractant. Using the SASSE spreadsheet to calculate the Cs decontamination factor (D.F.) as well as ROTOR program to evaluate contactor rotor throughputs, an initial flowsheet was developed for the optimized NG-CSSX solvent. The current CSSX flowsheet for the MCU at its maximum salt feed flow rate is shown in Fig. 1. The initial NG-CSSX flowsheet for the MCU at its targeted maximum salt feed flow rate is shown in Fig. 2. Comparing these two flowsheets, one can see that, for the NG-CSSX flowsheet, the maximum flow rate of the salt feed (DF) is increased 41%, from 8.5 gpm to 12.0 gpm. At the same time, because of the high concentration of the MaxCalix extractant in the NG-CSSX solvent, the Cs decontamination factor (D.F.) for the aqueous effluent from the extraction section (DW, the aqueous raffinate) is increased from 12 to 40,000. As required, the Cs concentration factor (C.F.) for the aqueous effluent from the strip section (EW) is maintained at 15.

Leonard, Ralph↗
DOE OSTI2012

Optimization and quality assurance of an image-guided radiation therapy system for intensity-modulated radiation therapy radiotherapy

To develop a quality assurance (QA) of XVI cone beam system (XVIcbs) for its optimal imaging-guided radiotherapy (IGRT) implementation, and to construe prostate tumor margin required for intensity-modulated radiation therapy (IMRT) if IGRT is unavailable. XVIcbs spatial accuracy was explored with a humanoid phantom; isodose conformity to lesion target with a rice phantom housing a soap as target; image resolution with a diagnostic phantom; and exposure validation with a Radcal ion chamber. To optimize XVIcbs, rotation flexmap on coincidency between gantry rotational axis and that of XVI cone beam scan was investigated. Theoretic correlation to image quality of XVIcbs rotational axis stability was elaborately studied. Comprehensive QA of IGRT using XVIcbs has initially been explored and then implemented on our general IMRT treatments, and on special IMRT radiotherapies such as head and neck (H and N), stereotactic radiation therapy (SRT), stereotactic radiosurgery (SRS), and stereotactic body radiotherapy (SBRT). Fifteen examples of prostate setup accounted for 350 IGRT cone beam system were analyzed. IGRT accuracy results were in agreement {+-} 1 mm. Flexmap 0.25 mm met the manufacturer's specification. Films confirmed isodose coincidence with target (soap) via XVIcbs, otherwise not. Superficial doses were measured from 7.2-2.5 cGy for anatomic diameters 15-33 cm, respectively. Image quality was susceptible to rotational stability or patient movement. IGRT using XVIcbs on general IMRT treatments such as prostate, SRT, SRS, and SBRT for setup accuracy were verified; and subsequently coordinate shifts corrections were recorded. The 350 prostate IGRT coordinate shifts modeled to Gaussian distributions show central peaks deviated off the isocenter by 0.6 {+-} 3.0 mm, 0.5 {+-} 4.5 mm in the X(RL)- and Z(SI)-coordinates, respectively; and 2.0 {+-} 3.0 mm in the Y(AP)-coordinate as a result of belly and bladder capacity variations. Sixty-eight percent of confidence was within {+-} 4.5 mm coordinates shifting. IGRT using XVIcbs is critical to IMRT for prostate and H and N, especially SRT, SRS, and SBRT. To optimize this modality of IGRT, a vigilant QA program is indispensable. Prostate IGRT reveals treatment accuracy as subject to coordinates' adjustments; otherwise a 4.5-mm margin is required to allow for full dose coverage of the clinical target volume, notwithstanding toxicity to normal tissues.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Placement and Sizing of Inverter-Based Renewable Systems in Multi-Phase Distribution Networks

This study develops a tractable formulation for optimal placement and sizing of inverter-based renewable systems in multi-phase distribution networks. The goal of the formulation is to minimize the cost of inverter installation, average power import, and average distributed generation curtailment. Threephase and single-phase inverter models are presented that preserve the underlying mappings between renewable uncertainty to power injection. The uncertainty of distributed generators (DGs) and loads are characterized by a finite set of scenarios. Linear multi-phase power flow approximations are used in conjunction with scenario reduction techniques to arrive at a tractable twostage stochastic formulation for optimal DG placement and sizing. First-stage decisions are locations for DG deployment and capacity sizes, and second-stage decisions include DG real power curtailment, reactive power support, as well as feeder voltage profile. The resulting formulation is a mixed-integer second-order cone program and can be solved efficiently either by existing optimization solvers or by relaxing the binary variables to the [0,1] interval. Simulation studies on standard multi-phase IEEE test feeders promise that optimal stochastic planning of DGs reduces costs during validation, compared to a scheme where uncertainty is only represented by its average value.

24 POWER TRANSMISSION AND DISTRIBUTION↗