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DOE OSTI2016

A Decision Analysis Tool for Climate Impacts, Adaptations, and Vulnerabilities

Climate change related extreme events (such as flooding, storms, and drought) are already impacting millions of people globally at a cost of billions of dollars annually. Hence, there are urgent needs for urban areas to develop adaptation strategies that will alleviate the impacts of these extreme events. However, lack of appropriate decision support tools that match local applications is limiting local planning efforts. In this paper, we present a quantitative analysis and optimization system with customized decision support modules built on geographic information system (GIS) platform to bridge this gap. This platform is called Urban Climate Adaptation Tool (Urban-CAT). For all Urban-CAT models, we divide a city into a grid with tens of thousands of cells; then compute a list of metrics for each cell from the GIS data. These metrics are used as independent variables to predict climate impacts, compute vulnerability score, and evaluate adaptation options. Overall, the Urban-CAT system has three layers: data layer (that contains spatial data, socio-economic and environmental data, and analytic data), middle layer (that handles data processing, model management, and GIS operation), and application layer (that provides climate impacts forecast, adaptation optimization, and site evaluation). The Urban-CAT platform can guide city and county governments in identifying and planning for effective climate change adaptation strategies.

adaptation↗

DOE OSTI2019

FAST: Using Federal Fleet Data for Decision-Making

This presentation provides an overview of the information currently being collected by the US Department of Energy, US General Services Administration, and US Energy Information Administration about the federal government's fleet of motor vehicles. The recent transition to collecting this information at the individual vehicle level provides new ways of visualizing the fleet and its operation and of using the information to support new forms of data-driven decision-making about the fleet. This presentation provides three examples of the types of analysis and decision-making this dataset supports.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2016

Role of Human Intrusion in Decision-Making for Radioactive Waste Disposal - Results of the IAEA HIDRA Project - 16287

Disposal of radioactive waste aims to protect people and the environment from the potential hazards of the materials by removing the wastes from the human environment. This approach is considered desirable to dispersal of the waste in the environment. However, the waste becomes concentrated in one location, which poses potential hazards should the waste become disturbed at a future date. For radioactive waste, it is generally expected that in preparing safety cases for such disposal facilities, the possibility of the wastes being inadvertently disturbed at some point in the future and the potential consequences of such an intrusion need to be considered. Potential inadvertent human intrusion (IHI) has a role in the decision-making and the safety case throughout the lifecycle of a disposal facility. In 2013, the International Atomic Energy Agency (IAEA) formally initiated a project entitled 'Human Intrusion in the Context of Disposal of Radioactive Waste' (HIDRA) to provide insights regarding the role of IHI for these decisions and to provide a common framework for consideration of IHI as part of development of a safety case for a disposal facility. HIDRA was implemented with a series of annual plenary meetings involving participants from more than 20 countries organised into three working groups. The working groups addressed: societal factors, stylised scenarios, and protective measures. The project resulted in a general approach with specific suggestions for consideration of IHI during the lifecycle of a disposal facility. From a general perspective, it was emphasized that IHI is a contributing factor to the safety case, but does not outweigh considerations related to safety of operations and long-term performance for the normal evolution of the facility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

The Ambivalent Role of Participants in Nuclear Waste Decisions - 16360

Today's global nuclear sector has entered a critical juncture. Waste management solutions have the potential to influence the prospects of whether new plants will be built and when aging plants will be decommissioned. Even in countries without nuclear power plants, the siting of nuclear waste can present communication challenges and opportunities for public involvement. As new nuclear programs withstand scrutiny, local communities share their want for solutions to nuclear waste before new plant construction commences. Developing nations have begun to explore the potential of adding nuclear power production to their energy mix. They carefully watch established nuclear players to accelerate their plans with lessons learned elsewhere. Some newcomers to nuclear energy will make decisions on new construction based on other operators' abilities to reconcile public concerns regarding radioactive waste. Distinct forms of government, cultural norms and means of public discourse present innovative approaches and changing expectations worldwide. This paper explores how practitioners are evaluating ways to activate stakeholders on nuclear waste siting in nations embarking on new nuclear programs. It investigates the incentives that people have to participate and legitimize the formation of laws, regulations and standards for nuclear materials management. Finally, it highlights examples of public participation techniques from established operating environments and posits that overly prescriptive requirements for stakeholder engagement can reduce the authenticity of public involvement and weaken confidence among participants in the decision making process. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Hydrologic Model Analyses for Decision Support at the Los Alamos National Laboratory Site - 18363

Modeling hydrologic flow and transport in the subsurface presents a number of challenges including large datasets that contain limited information about the properties of the subsurface and models with a large number of parameters characterizing the properties of the subsurface and the ambient hydrologic conditions (e.g., groundwater recharge). This, in turn, leads to substantial uncertainty in predictions about quantities of interest (e.g., the concentration of a contaminant at points of compliance). We present a number of methods and tools that have recently been developed to address these problems. The model calibration tools are capable of assimilating large datasets (for example, on the order of 1,000,000 model calibration targets) into numerical models, calibrating models with a large number of model parameters (on the order of 1,000,000), or both. These tools leverage recently developed methods in numerical linear algebra such as matrix-free methods and randomized matrix sketching. The uncertainty quantification tools are highly-parallel and enable us to robustly quantify the uncertainties in model parameters (and therefore uncertainty in quantities of interest). Having these capabilities in model analysis tools enables more informed decision making related to remediation of groundwater contaminants. The model analyses that we present are related to the Los Alamos National Laboratory (LANL) chromium site. The analyses are performed by MADS (Model Analysis and Decision Support; http://mads.lanl.gov). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Rejoinder to “Graphics to facilitate informative discussion and team decision-making”

We would like to thank Dr Dae-Heung Jang and Dr Jennifer Van Mullekom for their thoughtful discussions. We are encouraged that they share our vision for highlighting and expanding the role of graphics for improving team discussion and decision-making. Here we would like to respond to several comments that they made in their discussions: (1) how the availability of the third dimension in graphics can improve team members' ability to properly visualize and connect with the data, (2) the importance of developing dynamic and interactive graphics to allow exploration of questions as they emerge during the discussion, (3) the role of process and relationships in helping reach good decisions based on the developed objectives, and (4) the benefits of building a body of case studies on which those new to the methods can draw understanding.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

K-Spin Hamiltonian for Quantum-Resolvable Markov Decision Processes

The Markov decision process is the mathematical formalization underlying the modern field of reinforcement learning when transition and reward functions are unknown. We derive a pseudo-Boolean cost function that is equivalent to a K-spin Hamiltonian representation of the discrete, finite, discounted Markov decision process with infinite horizon. This K-spin Hamiltonian furnishes a starting point from which to solve for an optimal policy using heuristic quantum algorithms such as adiabatic quantum annealing and the quantum approximate optimization algorithm on near-term quantum hardware. In arguing that the variational minimization of our Hamiltonian is approximately equivalent to the Bellman optimality condition for a prevalent class of environments we establish an interesting analogy with classical field theory. Along with proof-of-concept calculations to corroborate our formulation by simulated and quantum annealing against classical Q-Learning, we analyze the scaling of physical resources required to solve our Hamiltonian on quantum hardware.

Hamiltonian↗

DOE OSTI2021

A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system

Hydro-wind-photovoltaic (PV) hybrid system has the potential to increase the integration of renewable energy sources into an existing grid. For the long-term operation of the system, due to the non-storable nature of wind and PV power, it is essentially to decide the optimal long-term carryover storage of cascade reservoirs. However, it remains a challenge due to high uncertainties of long-term forecasts and complicated hydraulic/electrical relationships between cascade reservoirs. Here in this study, a forecast-driven decision-making model is proposed for the hybrid system, which converts the multi-stage long-term operation process into a two-stage operation problem (including current stage and carryover stage) to avoid using longer-horizon forecast information with lower accuracy. First, the carryover stage energy surfaces (CESs) considering the forecast uncertainties of wind, solar and hydro resources are proposed to characterize carryover stage benefit quantitatively. Then a CESs-based forward decision-making optimization model is developed to guide the long-term operation of a hydro-wind-photovoltaic hybrid system. The applications in a hydro-wind-PV hybrid system of Yalong River basin results show that: compared with conventional operation, 1) power generation increases 9.03%; 2) in terms of the carryover storages control, the reservoir impounding and drawdown timing are delayed, and the drawdown depth is increased, which can be used to formulate better reservoir operation rules.

13 HYDRO ENERGY↗

DOE OSTI2022

Optimizing equity in energy policy interventions: A quantitative decision-support framework for energy justice

This paper presents a quantitative framework to support policy decision-making around equitable energy interventions. By combining sociodemographic and techno-economic models in the energy space, we propose a linear programming model to calculate the optimal portfolio of energy investments that explicitly minimizes the energy burden of a given population of energy insecure households. The model is formulated as a multi-objective optimization suitable to support the decisions on weatherization and deployment of distributed energy resources. We illustrate our methodology with a case study involving a population of 14,043 energy insecure households in Wayne County, Detroit, United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2015

Exploring the decision-space for renewable energy generation to enhance spatial efficiency

Decentralized power generation can play a significant role in contributing to renewable energy (RE) supply. Accordingly, regions can be important players in the transformation of the energy system. However, only scarce spatial capacities are available for a sustainable RE generation. Knowledge gaps exist concerning data and methods for integrating RE assessment and environmental planning methods. This paper presents a methodology for the integrated assessment of different RE potentials and their land requirements. The potential is contrasted with the actual availability of land for RE-generation considering environmental restrictions. An application in the Hanover region demonstrates that using energy capacity maps supports using the most efficient mixture in RE generation. Generally, a combination of wind and solar energies produces the highest energy yield per ha. Furthermore, relying primarily on generalized environmental restrictions for defining exclusion areas can be only a first step: On the one hand the assessment is probably underrating the potential for environmentally sound RE generation due to the undifferentiated exclusion of large areas of a certain protective status. On the other hand, some energy sources, in particular energy crops for bioenergy generation, are malpositioned due to missing spatially explicit information about ecosystem sensitivities and a lack of regulative possibilities. Further research is needed to explore synergistic combinations of energy potentials and their environmentally sound spatial allocation in more detail. - Highlights: • We combine models of RE potentials with technical and environmental restrictions. • We explore the resulting “decision-spaces” for regional RE development. • “Decision-spaces” are small and need to be developed carefully to avoid trade-offs. • Planning needs more differentiated analyses to test RE allocation-scenarios. • We recommend detailed analyses on (cumulative) impacts of (multifunctional) RE use.

14 SOLAR ENERGY↗

DOE OSTI2021

Decisions and coordination of retailer-led low-carbon supply chain under altruistic preference

A low-carbon supply chain formed by a dominant retailer and a small and medium-sized manufacturer (SMM) is considered. Because the SMM faces the high cost pressure of adopting carbon emission reduction (CER) technologies, the retailer may take an altruistic preference for the long-term sustainability of the chain. Three decision-making models, centralized, decentralized without altruistic preference, and decentralized with altruistic preference, are constructed to compare decisions and profits of both parties. Since neither decentrailized models can lead to a coordinated solution that is incentive compitable, a coordination contract, referred to as the cost sharing contract with altruistic preference, is proposed. Numerical study shows that the altruistic preference can help increase the SMM's profit and system efficiency but decrease the retailer's profit. The coordination contract requires the retailer to adjust the unit profit and share more than half of the CER cost. Finally, it is found that the wholesale price is the lowest in the coordination contract and the comparison of unit profit depends on the coefficient of CER cost.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Industrial battery operation and utilization in the presence of electrical load uncertainty using Bayesian decision theory

Behind the meter battery storage is becoming increasing popular in all sectors, though enthusiasm has recently lagged in the industrial sector. Even though there may be many factors contributing to this including lack of innovation, prohibitive costs, and undesirable rate structures, a difficulty arises in accounting for uncertainty of electrical load in industrial facilities while still attempting to utilize battery storage as much as possible all while trying to achieve fiscal profitability. Here this study utilizes Gaussian process regression and Bayesian decision theory to organize load data and quantify electrical load uncertainty to properly and effectively discharge industrial battery storage. The study employs a simulation model to set battery load setpoints for the span of the utility billing period according to the degree of risk aversion. This combination of economic analysis according to utility billing period and utilization of degree of risk aversion to make decisions on the uncertainty of the data has not before been applied to battery storage. The method resulted in an annual average reduction of peak demand by 3.8 % at the lowest amount of savings and lowest risk aversion. The highest risk aversion resulted in an annual average reduction of peak demand of 7.5 %. The maximum reduction of peak load in any month was 13.8 % in the month of December with a relatively high risk aversion. With a the highest amount risk aversion tested, the model reduced demand ten of the twelve months of the year.

25 ENERGY STORAGE↗

DOE OSTI2013

Decision Regret in Men Undergoing Dose-Escalated Radiation Therapy for Prostate Cancer

Purpose: Decision regret (DR) is a negative emotion associated with medical treatment decisions, and it is an important patient-centered outcome after therapy for localized prostate cancer. DR has been found to occur in up to 53% of patients treated for localized prostate cancer, and it may vary depending on treatment modality. DR after modern dose-escalated radiation therapy (DE-RT) has not been investigated previously, to our knowledge. Our primary aim was to evaluate DR in a cohort of patients treated with DE-RT. Methods and Materials: We surveyed 257 consecutive patients with localized prostate cancer who had previously received DE-RT, by means of a validated questionnaire. Results: There were 220 responses (85.6% response rate). Image-guided intensity modulated radiation therapy was given in 85.0% of patients and 3-dimensional conformal radiation therapy in 15.0%. Doses received included 73.8 Gy (34.5% patients), 74 Gy (53.6%), and 76 Gy (10.9%). Neoadjuvant androgen deprivation (AD) was given in 51.8% of patients and both neoadjuvant and adjuvant AD in 34.5%. The median follow-up time was 23 months (range, 12-67 months). In all, 3.8% of patients expressed DR for their choice of treatment. When asked whether they would choose DE-RT or AD again, only 0.5% probably or definitely would not choose DE-RT again, compared with 8.4% for AD (P<.01). Conclusion: Few patients treated with modern DE-RT express DR, with regret appearing to be lower than in previously published reports of patients treated with radical prostatectomy or older radiation therapy techniques. Patients experienced more regret with the AD component of treatment than with the radiation therapy component, with implications for informed consent. Further research should investigate regret associated with individual components of modern therapy, including AD, radiation therapy and surgery.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

Overcoming barriers to improved decision-making for battery deployment in the clean energy transition

Decarbonization plans depend on the rapid, large-scale deployment of batteries to sufficiently decarbonize the electricity system and on-road transport. This can take many forms, shaped by technology, materials, and supply chain selection, which will have local and global environmental and social impacts. Current knowledge gaps limit the ability of decision-makers to make choices in facilitating battery deployment that minimizes or avoids unintended environmental and social consequences. These gaps include a lack of harmonized, accessible, and up-to-date data on manufacturing and supply chains and shortcomings within sustainability and social impact assessment methods, resulting in uncertainty that limits incorporation of research into policy making. These gaps can lead to unintended detrimental effects of large-scale battery deployment. To support decarbonization goals while minimizing negative environmental and social impacts, we elucidate current barriers to tracking how decision-making for large-scale battery deployment translates to environmental and social impacts and recommend steps to overcome them.

25 ENERGY STORAGE↗

DOE OSTI2020

Impact of climate change on adaptive management decisions in the face of water scarcity

Reoccurring drought through the early 2000s has caused a serious water scarcity issue in the Colorado River Basin. Previous modeling studies have focused on the impact of climate change without considering the adaptive behaviors of farmers and under-utilized Indian water rights. Here, we use a coupled agent-based water resource model (ABM) to investigate how the adaptive decisions of farmers can affect water resource management under both climate change impacts and fully utilized Indian water right conditions. We used five General Circulation Model projections with RCP8.5 scenarios for the study. The results of farm-level decision-making showed different responses in irrigated areas that were changing due to climate change impact. While winter precipitation changes might partially explain the behavior changes, no specific pattern could be concluded based on their location. Also, farmers’ responses about annual water diversion showed more significant inter-year variation compared to irrigated areas. Basin-level metrics showed that climate change impacts will generally worsen water scarcity issues as measured in Navajo Reservoir storage, flow to Lake Powell, and instream flow requirement. But these basin-level water scarcity metrics cannot reflect individual farm-level impacts under climate change, which is why modeling the bottom-up management actions is necessary. When the under-utilized Indian water rights are fully used, it is more likely to trigger the shortage sharing agreement due to the higher tribal water depletion. Evaluation of model uncertainty and a more realistic setup for adaptive actions under drought contingency plans are suggested for future research.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

A high resolution agent-based model to support walk-bicycle infrastructure investment decisions: A case study with New York City

Active transportation modes--walk and bicycle--are central for low carbon transport, healthy living, and complete streets initiative. Building a community with amenable walk and bicycle facilities asks for smart planning and investments. It is critical to investigate the impact of infrastructure building or expansion on the overall walk and bicycle mode usage prior to making investment choices utilizing public tax money. This research developed an agent-based model to support investment decisions that allows to assess the impact of changes in walk-bike infrastructures at a high spatial resolution (e.g., block group level). The agent-based model (ABM) utilizes data from a synthetic population simulator generating agents with corresponding socio-demographic characteristics, and integrates facility attributes regarding walking and bicycling (e.g., sidewalk width, bike lane length) into the mode choice decision making process. Moreover, the ABM accounts for the effect of social interactions among agents who live and work at the same geographic locations. Finally, GIS-based maps are developed at block group resolution that allows exploring the effect of walk-bike infrastructure related investments. The results from New York City case study indicate that infrastructure investments such as widening sidewalk and increasing bike lane network can positively influence the active transportation mode choices. In addition, the level of impact varies with geographic locations--different boroughs of New York City will have different impacts. Lastly, social promotions resulting in higher social interaction among agents can reinforce the impacts of infrastructure changes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Effects of Climate Change on Capacity Expansion Decisions of an Electricity Generation Fleet in the Southeast U.S.

The electric power sector in the United States faces many challenges related to climate change. On the demand side, climate change could shift demand patterns due to increased air temperatures. On the supply side, climate change could lead to deratings of thermal units due to changes in air temperature, water temperature, and water availability. Past studies have typically analyzed these risks separately. Here, we developed an integrated, multimodel framework to analyze how compounding risks of climate-change impacts on demand and supply affect long-term planning decisions in the power system. In the southeast U.S., we found that compounding climate-change impacts could result in a 35% increase in installed capacity by 2050 relative to the reference case. Participation of renewables, particularly solar, in the fleet increased, driven mostly by the expected increase in summertime peak demand. Such capacity requirements would increase investment costs by approximately 31 billion (USD 2015) over the next 30 years, compared to the reference case. These changes in investment decisions align with carbon emission mitigation strategies, highlighting how adaptation and mitigation strategies can converge.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Climate modeling with decision makers in mind

The need for regional- and local-scale climate information is increasing rapidly as decision makers seek to anticipate and manage a variety of context-specific climate risks over the next several decades. However, global climate models are not developed with these user needs in mind, and they typically operate at resolutions that are too coarse to provide information that could be used to support regional and local decisions.

54 ENVIRONMENTAL SCIENCES↗