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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hydropower Potential at Non-Powered Dams: A Multi-Criteria Decision Analysis Tool based on Grid, Community, Industry, and Environmental Impacts

Non-powered dams (NPDs) are dams that do not include hydraulic turbine (hydropower) equipment. Currently, there are more than 80,000 such dams in the United States, which provide a variety of non-energy benefits, including flood control, water supply, navigation, and recreation. Approximately 500 of these NPDs are identified as having the potential to add hydropower generation (totaling up to a capacity of more than 8200 MW). A large share of investment costs and environmental impacts of dam construction have already been incurred at these NPDs. Hence, adding power to the existing dam structure is hypothesized to be achieved at a lower cost, with less risk, and a shorter timeframe than the development required for new dam construction. The abundance of NPDs, the associated environmental favorability, and cost advantages, combined with the reliability, predictability, and dispatchability of hydropower, make NPDs a strong candidate in the nation’s renewable energy portfolio. To assess the NPD to hydropower conversion potential, in this study, we developed a GIS-based multi-criterial decision analysis tool, which allows users to rank these NPDs based on the grid, community, industry, and environmental impacts (i.e., GCIE impacts). This web-based interactive tool (developed using open-source Python and JavaScript) lets the user choose from a wide range of features to define each of the GCIE impact scores through a user-friendly graphical user interface. These features are related to dam operation, hydropower generation opportunity, power market economy, social vulnerability and risk, proximity to critical infrastructure and energy generating facilities, environmental concerns (air, water, and critical habitat), and exposure to natural hazards. The overall priority score of NPDs is calculated based on user-defined weights for each of the GCIE impact scores. Besides ranking NPDs, the tool can also be used to estimate the energy-storage feasibility (battery, hydrogen, and pump-storage hydropower) at each of the potential sites.

13 HYDRO ENERGY↗
DOE OSTI2023

Application of multi-criteria decision analysis techniques and decision support framework for informing plant select agent designation and decision making

The United States Department of Agriculture (USDA) Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents (Select Agents List) that threaten crops of economic importance to the United States and regulates the procedures governing containment, incident response, and the security of entities working with them. Every 2 years the USDA DASAT reviews their select agent list, utilizing assessments by subject matter experts (SMEs) to rank the agents. We explored the applicability of multi-criteria decision analysis (MCDA) techniques and a decision support framework (DSF) to support the USDA DASAT biennial review process. The evaluation includes both current and non-select agents to provide a robust assessment. We initially conducted a literature review of 16 pathogens against 9 criteria for assessing plant health and bioterrorism risk and documented the findings to support this analysis. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for ensuring accuracy. Scoring criteria were adopted to ensure consistency. The MCDA supported the expectation that select agents would rank high on the relative risk scale when considering the agricultural consequences of a bioterrorism attack; however, application of analytical thresholds as a basis for designating select agents led to some exceptions to current designations. A second analytical approach used agent-specific data to designate key criteria in a DSF logic tree format to identify pathogens of low concern that can be ruled out for further consideration as select agents. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens

The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and training requirements for entities working with them. Every 2 years the USDA DASAT reviews the Select Agent List, using subject matter experts (SMEs) to perform an assessment and rank the agents. To assist the USDA DASAT biennial review process, we explored the applicability of multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) in a logic tree format to identify pathogens for consideration as select agents, applying the approach broadly to include non-select agents to evaluate its robustness and generality. We conducted a literature review of 41 pathogens against 21 criteria for assessing agricultural threat, economic impact, and bioterrorism risk and documented the findings to support this assessment. The most prominent data gaps were those for aerosol stability and animal infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. The MCDA analysis supported the intuitive sense that select agents should rank high on the relative risk scale when considering agricultural health consequences of a bioterrorism attack. However, comparing select agents with non-select agents indicated that there was not a clean break in scores to suggest thresholds for designating select agents, requiring subject matter expertise collectively to establish which analytical results were in good agreement to support the intended purpose in designating select agents. The DSF utilized a logic tree approach to identify pathogens that are of sufficiently low concern that they can be ruled out from consideration as a select agent. In contrast to the MCDA approach, the DSF rules out a pathogen if it fails to meet even one criteria threshold. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making

The Centers for Disease Control and Prevention (CDC) Select Agent Program establishes a list of biological agents and toxins that potentially threaten public health and safety, the procedures governing the possession, utilization, and transfer of those agents, and training requirements for entities working with them. Every 2 years the Program reviews the select agent list, utilizing subject matter expert (SME) assessments to rank the agents. In this study, we explore the applicability of multi-criteria decision analysis (MCDA) techniques and logic tree analysis to support the CDC Select Agent Program biennial review process, applying the approach broadly to include non-select agents to evaluate its generality. We conducted a literature search for over 70 pathogens against 15 criteria for assessing public health and bioterrorism risk and documented the findings for archiving. The most prominent data gaps were found for aerosol stability and human infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. Analysis of results obtained from a two-dimensional plot of weighted scores for difficulty of attack (i.e., exposure and production criteria) vs. consequences of an attack (i.e., consequence and mitigation criteria) provided greater fidelity for understanding agent placement compared to a 1-to-n ranking and was used to define a region in the upper right-hand quadrant for identifying pathogens for consideration as select agents. A sensitivity analysis varied the numerical weights attributed to various properties of the pathogens to identify potential quantitative (x and y) thresholds for classifying select agents. The results indicate while there is some clustering of agent scores to suggest thresholds, there are still pathogens that score close to any threshold, suggesting that thresholding “by eye” may not be sufficient. The sensitivity analysis indicates quantitative thresholds are plausible, and there is good agreement of the analytical results with select agent designations. A second analytical approach that applied the data using a logic tree format to rule out pathogens for consideration as select agents arrived at similar conclusions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Modeling Electric Vehicle Charging Station Siting Suitability with a Focus on Equity

As adoption of electric vehicles increases, the infrastructure to charge them must keep pace. Determining where to add new charging infrastructure is a complex process subject to many factors, including electrical service availability, vehicle dwell time, the type(s) of drivers and vehicles the stations will serve, traffic levels and timing, and land ownership. In addition, advancing social equity is a current priority of federal efforts to invest in electric vehicle charging infrastructure. Conducting Multi-criteria Decision Analysis (MCDA) within Argonne’s Energy Zones Mapping Tool (EZMT) is a useful method for analyzing many of the factors that influence how suitable a location is for potentially adding new charging infrastructure, and we show how equity metrics can be included in the analysis. However, data limitations impose challenges to using MCDA to evaluate and prioritize locations. We use three examples to demonstrate how to use publicly available data and MDCA to analyze different siting objectives. Each example starts with defining a specific objective and ends with how to use the results to identify specific potential locations that could be investigated further. This analysis demonstrates how interested stakeholders can use the EZMT to run the example MCDA models defined in this study, modify them to suit their needs, or create new MCDA models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

The multi-criteria decision analysis (MCDA) framework provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. This approach is advantageous over a single-criterion methodology, which are only valid under conditions that assume ceteris paribus or an "apples-to-apples" comparison. However, selecting suitable technologies for integrated energy systems (IES) can be likened to an "apples-to-oranges" comparison, given the heterogeneous factors at stake. These factors include economics and performance parameters, geological compatibility, and environmental impacts. Consequently, past research has often employed a mixture of qualitative and quantitative criteria tailored to the specific interests of each study. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. This inconsistency is introduced from the use of subjectively defined thresholds and weights. As a result, decision-makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. We leveraged state-of-the-art cost estimates from the Idaho National Laboratory (INL) and technical data from 78 reactor designs from the International Atomic Energy Agency (IAEA) and the Organization for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA). Lastly, we established thresholds by analyzing the mean, variance, root mean square, and slope of values across alternatives, categorizing the preferences of decision-makers into distinct utility functions, such as linear, saturating, exponential, and stepwise. Our approach yielded two main outcomes: (1) it provided consistent assessments across different stakeholder groups and (2) it visualized uncertainties in the decision-making context via comprehensive sensitivity analysis. To demonstrate the impact of our framework, we conducted case studies on 6 reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our data-driven framework proved to be highly effective in addressing heterogenous uncertainties faced by varied decision-makers? preferences and IES applications, as well as cost and technical estimates of advanced reactors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

The multi-criteria decision analysis (MCDA) framework provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. This approach is advantageous over a single-criterion methodology, which are only valid under conditions that assume ceteris paribus or an "apples-to-apples" comparison. However, selecting suitable technologies for integrated energy systems (IES) can be likened to an "apples-to-oranges" comparison, given the heterogeneous factors at stake. These factors include economics and performance parameters, geological compatibility, and environmental impacts. Consequently, past research has often employed a mixture of qualitative and quantitative criteria tailored to the specific interests of each study. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. This inconsistency is introduced from the use of subjectively defined thresholds and weights. As a result, decision-makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. We leveraged state-of-the-art cost estimates from the Idaho National Laboratory (INL) and technical data from 78 reactor designs from the International Atomic Energy Agency (IAEA) and the Organization for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA). Lastly, we established thresholds by analyzing the mean, variance, root mean square, and slope of values across alternatives, categorizing the preferences of decision-makers into distinct utility functions, such as linear, saturating, exponential, and stepwise. Our approach yielded two main outcomes: (1) it provided consistent assessments across different stakeholder groups and (2) it visualized uncertainties in the decision-making context via comprehensive sensitivity analysis. To demonstrate the impact of our framework, we conducted case studies on 6 reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our data-driven framework proved to be highly effective in addressing heterogenous uncertainties faced by varied decision-makers? preferences and IES applications, as well as cost and technical estimates of advanced reactors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2017

Sensitivity Analysis Method to Address User Disparities in the Analytic Hierarchy Process

Decision makers often face complex problems, which can seldom be addressed well without the use of structured analytical models. Mathematical models have been developed to streamline and facilitate decision making activities, and among these, the Analytic Hierarchy Process (AHP) constitutes one of the most utilized multi-criteria decision analysis methods. While AHP has been thoroughly researched and applied, the method still shows limitations in terms of addressing user profile disparities. A novel sensitivity analysis method based on local partial derivatives is presented here to address these limitations. This new methodology informs AHP users of which pairwise comparisons most impact the derived weights and the ranking of alternatives. The method can also be applied to decision processes that require the aggregation of results obtained by several users, as it highlights which individuals most critically impact the aggregated group results while also enabling to focus on inputs that drive the final ordering of alternatives. An aerospace design and engineering example that requires group decision making is presented to demonstrate and validate the proposed methodology.

Marie Ivanco↗
DOE OSTI2025

Where to cool off: a geospatial framework for placement of cooling centers

Indoor cooling is essential to reduce heat stress and increase passive survivability during heatwaves. Although air conditioning (AC) is recommended for maintaining indoor thermal comfort, low- and medium-income households in the U.S. often do not own an AC and/or limit AC usage to reduce energy consumption and associated costs, thereby risking their health and safety. With the frequency and intensity of heatwaves increasing, cooling centers are considered an appropriate alternative to indoor cooling and a possible mitigation strategy to prevent adverse health impacts of heat exposure. However, these centers are limited in numbers and not always accessible. This requires (i) developing a geospatial framework using physical and social factors for optimal siting of cooling centers to meet future needs and (ii) ranking of existing and potential cooling centers (schools, libraries, religious institutions) based on their accessibility among vulnerable populations and proximity to healthcare facilities. We developed and deployed a geospatial framework based on the Multi-criteria Decision Analysis approach in five U.S. cities (Los Angeles (LA), Phoenix, Austin, Atlanta, Miami) to evaluate the effectiveness of the framework in ranking cooling centers based on accessibility and population coverage. The results revealed that (i) access to cooling centers varies across cities and 32.2–50.7% of centers are within walking distance of the most vulnerable populations, (ii) vulnerable populations exposed to Urban Heat Island (UHI) effects are more likely to experience energy burden, and (iii) about 21.2–49.4% of population with high energy burden have access to these centers. Considering that more cooling centers are needed to assist energy burdened households alleviate heat exposure impacts, the framework developed herein could be adapted to incorporate other factors (e.g. health impacts, policies) to assess site suitability of existing shelters, identify potential sites for new cooling centers, and geo-target communities where energy efficient emerging technologies could be deployed to reduce heat stress.

58 GEOSCIENCES↗
NASA NTRS2014

Selecting an Architecture for a Safety-Critical Distributed Computer System with Power, Weight and Cost Considerations

This report presents an example of the application of multi-criteria decision analysis to the selection of an architecture for a safety-critical distributed computer system. The design problem includes constraints on minimum system availability and integrity, and the decision is based on the optimal balance of power, weight and cost. The analysis process includes the generation of alternative architectures, evaluation of individual decision criteria, and the selection of an alternative based on overall value. In this example presented here, iterative application of the quantitative evaluation process made it possible to deliberately generate an alternative architecture that is superior to all others regardless of the relative importance of cost.

Torres-Pomales, Wilfredo↗
DOE OSTI2024

GIS Supported Optimal Site Selection for Coastal Structure Integrated Wave Energy Converters: Preprint

There is an urgent need for adaptative engineering towards more resilient coastal communities, and Coastal Structure Integrated Wave Energy Converters (CSI-WECs) are a promising solution. CSI-WECs are wave energy converters (WECs) that are built into coastal protection structures, such as breakwaters. These devices provide the dual benefits of coastal protection and local energy production, and unlike other WECs, maximizing energy production is not always the main objective. CSI-WECs are located near the shore, where the wave resource is lower, thus site selection for these devices differs from the typical offshore WECs. Other attributes of a site that may be more important than wave power include existing coastal structures, port proximity, electric transmission line proximity, and location of disadvantaged communities. Geospatial information systems (GIS) interfaces can be used to easily visualize geospatial data that represents these difference kinds of criteria important for the determination of optimal marine energy sites. Multi-Criteria Decision Analysis (MCDA) is a geospatial analysis method that allows for the evaluation of multiple, usually overlapping, criteria. This project applies GIS-based MCDA methods to two distinct case studies in Puerto Rico and California for CSI-WEC site selection. The two study sites contrast in terms of wave resource, coastal hazards, and local energy needs. This research demonstrates the utility of applying an MCDA framework within GIS to facilitate efficient site selection for devices with unique characteristics in different use cases.

coastal protection↗
DOE OSTI2024

Plastic Parallel Pathways Platform - 4P Model

Global momentum is building towards a circular economy capable of keeping plastics in use and out of waste streams. Given that 79% of all plastic produced since 1950 has accumulated in landfills or the natural environment,rapid implementation of various end-of-life (EoL) management technologies will be needed to reach this target. However, it can be challenging to develop an effective plastic EoL strategy when the available options - chemical or molecular recycling, energy recovery, upcycling, downcycling, closed-loop (plastic-to-plastic) or open-loop (plastic-to-x) recycling, among others - can generate products ranging from low-grade to virgin-quality plastic and from fuels to value-added chemicals. We present a flexible material flow model capable of analyzing the effects of both plastic-to-plastic and plastic-to-x EoL management strategies on the U.S. PET economy. This Plastic Parallel Pathways Platform (4P) assesses the environmental impacts, costs, and circularity of a PET system in which waste is managed through six potential EoL pathways: landfill, incineration with energy recovery, pyrolysis to fuel oil, upcycling to glass fiber reinforced plastic (GFRP), mechanical recycling to low-grade PET, and chemical recycling (glycolysis) to bottle-grade PET. We compare the pathways across multiple metrics using multi-criteria decision analysis (MCDA) and then use a brute force algorithm to predict an optimal combination of EoL pathways to minimize greenhouse gas (GHG) emissions and costs and maximize circularity. This work highlights the need to implement a diverse portfolio of EoL strategies in parallel to enable a PET economy that meets environmental, economic, and circularity requirements simultaneously.

downcycling↗
DOE OSTI2026

Report on Coastal Structure Integrated Wave Energy Converters (CSI-WECs)

This report investigates a largely underexplored, and underexploited, application for wave energy: coastal structure integrated wave energy converters (CSI-WECs). These WEC types have significant potential to rapidly advance the wave energy industry while addressing the challenges of coastal community resiliency and marine energy integration and application, and the urgent need for increased coastal protection. To further support the value proposition investigation of this work, three wave energy developers agreed to conduct an energy production analysis of their technology for the four sites identified. Additionally, a geographic information system-based tool was developed that supports efficient and comprehensive site assessment for optimal and high-value deployments.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

A model for selecting the best sustainable airport technology alternatives

Air transport is a continually expanding industry, a fact that has become even more evident with the recovery of the aviation industry post-COVID-19. This expansion amplified energy consumption at airports, which was already significantly high. Airport decision-makers are increasingly focusing on improving sustainability and addressing social, economic, and environmental criteria across airports worldwide. In this article, we propose a decision-making model for identifying a sustainable airport technology solution that minimizes the energy consumption of airport lighting while considering economic, emission, and life-cycle criteria. The proposed model combines data envelopment analysis and multi-criteria decision making techniques. We applied the model to the Dallas/Fort Worth International Airport (DFW) as a case study, considering eight lighting technology solutions, each with five luminous flux alternatives. Our model identified the best lighting technology solution for DFW outdoor and indoor environments based on the following criteria: luminous flux, capital costs, life-cycle costs, energy consumption, and emissions (CO2e, NOx, SO2, and PM2.5). The designed model is customizable to any airport and is applicable to a wide range of airport lighting technologies. In our analysis, Light-Emitting Diode lighting emerged as the most sustainable technology option. It ranked first in most cases due to its balance of high efficacy, long lifespan, low life-cycle cost, low capital cost, and lower emissions across all pollutants.

Tchivwila, Moise B↗
NASA NTRS2001

Space Shuttle RTOS Bayesian Network

With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores. Using a prioritization of measures from the decision-maker, trade-offs between the scores are used to rank order the available set of RTOS candidates.

Morris, A. Terry↗
DOE OSTI2022

A multi-criteria CCUS screening evaluation of the Gulf of Mexico, USA

Continued research into reservoir characterization along with offshore carbon dioxide (CO 2 ) transportation and infrastructure assets is needed to facilitate development of safe and successful carbon capture, utilization, and storage (CCUS) projects. This paper outlines a multi-criteria evaluation methodology that incorporates disparate sets of quantitative, spatially variable data into a decision-making framework for screening the Gulf of Mexico (GOM) outer continental shelf (OCS) for potentially viable CO 2 storage and enhanced oil recovery (EOR) sites. Criteria categories include favorable geologic characteristics, logistics, and potential risks. Data compiled for 14 criteria from several publicly available geographic information system (GIS) layers was aggregated over 2559 spatially balanced points across the study area using the National Energy Technology Laboratory (NETL)-developed Cumulative Spatial Impact Layers™ (CSIL) GIS tool. Criteria are weighted by qualitative expert opinion relative to their perceived importance to given scenarios— the output of combined criteria values and weights enables regional CO 2 storage suitability differentiation. The methodology considers both technical and non-technical factors impacting CCUS decision-making. The flexible methodology enables a systematic approach to regional ranking at high spatial resolution over a large study domain. Additionally, the framework enables high-grading of priority sites that warrant further characterization and follow-on analysis. Areas along the Louisiana coast and Mississippi River Delta consistently rank high for all scenarios largely a result of the favorable geology with the potential for stacked storage, as well as the density of existing pipelines and platforms, and proximity to several onshore CO 2 sources. High-graded regions for the CO 2 EOR-related scenarios are typically located further offshore towards the middle and edge of the OCS compared to higher priority regions for the geologic storage scenarios which fall closer to the Louisiana coastline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Distributed Rules-Based Deconfliction of ADMS Applications - Part 2: Conceptual Implementation

This work introduces a rules-based deconfliction methodology for resolving conflicting device control commands issued by advanced power applications considering a range of technical, economic, environmental, and social objectives. The methodology is designed to serve as one of multiple alternative implementations (along with application cooperation and global optimization) for the numerical component of the Deconfliction Pipeline. Development of the methodology is divided into two parts. The first document previously defined the requirements, context, and methods for decomposing the deconfliction problem using the Laminar Coordination Framework and Variable Grid Structures. The deconfliction problem was decomposed into a distributed optimization problem based on the concept of quasi-static grid segments, which form independent distributed areas for control and coordination. It was recommended that selection of the optimal number of decompositions of the deconfliction problem be made based on a tradeoff analysis between computational speed and global optimality. This second document defines an initial set of technical, economic, and environmental criteria, as well as thirty specific qualitative rules that are used as part of the deconfliction methodology to eliminate non-viable setpoint alternatives. The deconfliction optimization problem is converted into a ranking of individual discrete setpoints, which are scored by the extent to which they satisfy specific decision criteria. The ranking is determined through the concepts of deconfliction exclusivity, priority, and preference. Several multi-criteria decision-making frameworks are examined with the simple multi-attribute rating technique exploiting ranks (SMARTER) recommended as a simple implementation alternative that aligns with the steps of the rules-based deconfliction methodology.

97 MATHEMATICS AND COMPUTING↗