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DOE OSTI2019

A Regional Model of Climate Change and Human Migration

As climate change and human migration accelerate globally, decision-makers are seeking tools that can deepen their understanding of the complex nexus between climate change and human migration. These tools can help to identify populations under pressure to migrate, and to explore proactive policy options and adaptive measures. Given the complexity of factors influencing migration, this article presents a system dynamics-based model that couples migration decision making and behavior with the interacting dynamics of economy, labor, population, violence, governance, water, food, and disease. The regional model is applied here to the test case of migration within and beyond Mali. As a result the study explores potential systems impacts of a range of proactive policy solutions and shows that improving the effectiveness of governance and increasing foreign aid to urban areas have the highest potential of those investigated to reduce the necessity to migrate in the face of climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Gnu-RL: A Precocial Reinforcement Learning Solution for Building HVAC Control Using a Differentiable MPC Policy

Reinforcement learning (RL) was first demonstrated to be a feasible approach to controlling heating, ventilation, and air conditioning (HVAC) systems more than a decade ago. However, there has been limited progress towards a practical and scalable RL solution for HVAC control. While one can train an RL agent in simulation, it is not cost-effective to create a model for each thermal zone or building. Likewise, existing RL agents generally take a long time to learn and are opaque to expert interrogation, making them unattractive for real-world deployment. To tackle these challenges, we propose Gnu-RL: a novel approach that enables practical deployment of RL for HVAC control and re- quires no prior information other than historical data from exist- ing HVAC controllers. To achieve this, Gnu-RL adopts a recently- developed Differentiable Model Predictive Control (MPC) policy, which encodes domain knowledge on planning and system dynam- ics, making it both sample-efficient and interpretable. Prior to any interaction with the environment, a Gnu-RL agent is pre-trained on historical data using imitation learning, which enables it to match the behavior of the existing controller. Once it is put in charge of controlling the environment, the agent continues to improve its policy end-to-end, using a policy gradient algorithm. We evaluate Gnu-RL on both an EnergyPlus model and a real- world testbed. In both experiments, our agents were directly de- ployed in the environment after offline pre-training on expert demonstration. In the simulation experiment, our approach saved 6.6% energy compared to the best published RL result for the same environment, while maintaining a higher level of occupant comfort. Next, Gnu-RL was deployed to control the HVAC of a real-world conference room for a three-week period. Our results show that Gnu-RL saved 16.7% of cooling demand compared to the existing controller and tracked temperature set-point better.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Leverage demand-side policies for energy security

Energy security is a top priority for governments, companies, and households because energy systems and the critical functions that they support are threatened by disruptions from wars, pandemics, climate change, and other shocks (1). More often than not, governments rely on policies focused on energy supply to enhance energy security while generally ignoring demand-side possibilities. Further, the indicators traditionally used to measure energy security are also tilted toward the supply side; this fails to capture the full spectrum of vulnerability to energy crises. Energy security assessments need to reflect the wider benefits of security related interventions more accurately. To that end, we develop a systematic approach to measuring the energy security impacts of policy interventions that explicitly considers energy demand (buildings, transport, and industry). Here, we determine that demand-side actions outperform conventional supply-side approaches at making countries more resilient. Energy demand links more directly than supply to the satisfaction of critical social functions and human well-being that are at the core of energy security. Yet, demand-side perspectives tend to be neglected or underrepresented in analysis and policy debates on energy security. Factors that contribute to this supply-side bias include the traditional sectoral organization of industries and policy institutions along fuels (coal, oil, and gas) and energy forms (electric utilities) as well as the decentralized and multivaried activities characteristic of energy demand (from vehicles to household appliances to manufacturing and more), which leads to a multitude of actors and institutional fragmentation. The basic fundamentals of energy systems and markets, where demand and supply are intricately linked, have also not yet risen from vague awareness to a central organizing principle among policy-makers for structuring the energy security discourse.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Clear Sky Tampa Bay: Project and Toolkit Overview - Resilience-Based Resources for Assessing Solar + Storage on Critical Facilities

The Clear Sky Tampa Bay Team, convened by the Tampa Bay Regional Planning Council (TBRPC) and composed of county and city partners, private and public sector stakeholders, solar energy experts, and academic partners, collaboratively developed a set of resources to support the expanded use of solar + storage in the context of community resilience in Florida. Key questions guiding the project included: (1) What types of buildings and systems provide the maximum resilience benefits? (2) Where within our communities should we prioritize future solar + storage analysis or investment to mitigate the impacts of disasters? (3) How might other societal variables be incorporated into a site assessment when considering solar + storage for resilience? (4) Can a standardized process be developed that leverages existing resources to assist local governments with solar + storage for resilience research and decision-making?

14 SOLAR ENERGY↗

DOE OSTI2022

Feasibility of critical infrastructure protection using network functions for programmable and decoupled ICS policy enforcement over WAN

Industrial control systems (ICS) represent a major component of our critical infrastructure. With the increasing need for more control and monitoring of such systems, ICS have seen an increase in connectivity to wide area networks (WAN) exposing aging equipment to rapidly evolving cybersecurity threats. Furthermore, the ICS data requires a reliability measure from the networks for critical functions for infrastructure monitoring and control. Especially when remote plant sites are involved such as pipelines, energy distribution networks, and transportation, WAN transport impairments most often provide a best effort delivery with no strict reliability guarantees. Network functions can provide a vendor agnostic, programmable critical infrastructure protection with a single maintenance, policy determination, and reliability assurance surface. A network function (NF) can be utilized for policy enforcement over the communication between remote entities and the main control office. This paper presents the research on transparent integration with existing ICS without disrupting communications, resulting in minimal downtime while decoupling the fast paced evolution of defensive security measures from the upgrade cycle of expensive long term hardware. We report our measurements on the resource requirements and overhead in the network for successful NF insertion under a wide variety of network impairments (network packet delay, reordering, and loss). Our paired NF implementation provides a policy enforcement platform extensible to cover myriad cybersecurity-related communication goals, including packet signing for verification, encryption for data privacy, packet filtering and data diode operation (i.e. protecting against eavesdropping, packet injection, and denial-of-service). Furthermore, bundling communication specifications into packet flows allows for tunability in applying policies as coarse- or fine-grained as the needs of the operator. We report on network function resource requirements in the form of required queue depth and network utilization overhead to inform the decision making against hardware cost constraints.

42 ENGINEERING↗

DOE OSTI2016

Big data analytics in the building industry

Catalyzed by recent market, technology, and policy trends, energy data collection in the building industry is becoming more widespread. This wealth of information allows more data-driven decision-making by designers, commissioning agents, facilities staff, and energy service providers during the course of building design, operation and retrofit. The U.S. Department of Energy’s Building Performance Database (BPD) has taken advantage of this wealth of building asset- and energy-related data by collecting, cleansing, and standardizing data from across the U.S. on over 870,00 buildings, and is designed to support building benchmarking, energy efficiency project design, and buildings-related policy development with real-world data. Here, this article explores the promises and perils energy professionals are faced with when leveraging such tools, presenting example analyses for commercial and residential buildings, highlighting potential issues, and discussing solutions and best practices that will enable designers, operators and commissioning agents to make the most of ‘big data’ resources such as the BPD.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1991

White paper on science operations

Major changes are taking place in the way astronomy gets done. There are continuing advances in observational capabilities across the frequency spectrum, involving both ground-based and space-based facilities. There is also very rapid evolution of relevant computing and data management technologies. However, although the new technologies are filtering in to the astronomy community, and astronomers are looking at their computing needs in new ways, there is little coordination or coherent policy. Furthermore, although there is great awareness of the evolving technologies in the arena of operations, much of the existing operations infrastructure is ill-suited to take advantage of them. Astronomy, especially space astronomy, has often been at the cutting edge of computer use in data reduction and image analysis, but has been somewhat removed from advanced applications in operations, which have tended to be implemented by industry rather than by the end-user scientists. The purpose of this paper is threefold. First, we briefly review the background and general status of astronomy-related computing. Second, we make recommendations in three areas: data analysis; operations (directed primarily to NASA-related activities); and issues of management and policy, believing that these must be addressed to enable technological progress and to proceed through the next decade. Finally, we recommend specific NASA-related work as part of the Astrotech-21 plans, to enable better science operations in the operations of the Great Observatories and in the lunar outpost era.

Schreier, Ethan J.↗

DOE OSTI2022

Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem

The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network. DED produces a more dynamic supervisory control policy than traditional economic dispatch (T-ED) that reduces overall generation costs. However, the incorporation of differential equations that govern the system dynamics makes DED an optimization problem that is computationally prohibitive to solve. In this work, we present a new data-driven approach based on differentiable programming to efficiently obtain offline parametric solutions to the underlying DED problem. In particular, we employ the recently proposed differentiable predictive control (DPC) for offline learning of explicit neural control policies based on identified Koopman operator (KO) model of the system dynamics. We demonstrate the high solution quality and five orders of magnitude computational-time savings of the DPC method over the original optimization-based DED approach on a 9-bus test power grid network.

King, Ethan↗

DOE OSTI2015

Adaptation of methodology to select structural alternatives of one-way slab in residential building to the guidelines of the European Committee for Standardization (CEN/TC 350)

The European Committee for Standardization (CEN) through its Technical Committee CEN/TC-350 is developing a series of standards for assessing the building sustainability, at both product and building levels. The practical application of the selection (decision making) of structural alternatives made by one-way slabs leads to an intermediate level between the product and the building. Thus the present study addresses this problem of decision making, following the CEN guidelines and incorporating relevant aspects of architectural design into residential construction. A life cycle assessment (LCA) is developed in order to obtain valid information for the decision making process (the LCA was developed applying CML methodology although Ecoindicator99 was used in order to facilitate the comparison of the values); this information (the carbon footprint values) is contrasted with other databases and with the information from the Environmental Product Declaration (EPD) of one of the lightening materials (expanded polystyrene), in order to validate the results. Solutions of different column disposition and geometries are evaluated in the three pillars of sustainable construction on residential construction: social, economic and environmental. The quantitative analysis of the variables used in this study enables and facilitates an objective comparison in the design stage by a responsible technician; the application of the proposed methodology reduces the possible solutions to be evaluated by the expert to 12.22% of the options in the case of low values of the column index and to 26.67% for the highest values. - Highlights: • Methodology for selection of structural alternatives in buildings with one-way slabs • Adapted to CEN guidelines (CEN/TC-350) for assessing the building sustainability • LCA is developed in order to obtain valid information for the decision making process. • Results validated comparing carbon footprint, databases and Env. Product Declarations • The proposal reduces the solutions to be evaluated to between 12.22 and 26.67%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

An overview of the Energy Modeling Forum 33rd study: Assessing large-scale global bioenergy deployment for managing climate change

Previous studies have projected a significant role for bioenergy in decarbonizing the global economy and helping realize international climate goals such as limiting global average warming to 2°C or 1.5°C. However, with significant variability in bioenergy results and significant concerns about potential environmental and social implications, greater transparency and dedicated assessment of the underlying modeling and results and more detailed understanding of the potential role of bioenergy are needed. Stanford University’s Energy Modeling Forum (EMF) initiated a 33rd study (EMF-33) to explore the viability of large-scale bioenergy as part of a comprehensive climate management strategy. This special issue presents the papers of the EMF-33 study—a multi-year inter-model comparison project designed to understand and assess global, long-run, biomass supply and bioenergy deployment potentials and related uncertainties. Using a novel scenario design with independent biomass supply and bioenergy demand protocols, EMF-33 separately elucidates and explores the modeling of biomass feedstock supplies and bioenergy technologies and their deployment—revealing, comparing, and assessing the modeling that is suggesting that bioenergy could be a key climate containment strategy. This introduction provides an overview of the EMF-33 study design and the overview, thematic, and individual modeling team papers and types of insights that make up this special issue. By providing enhanced transparency and new detailed insights, we hope to inform policy dialogue about the potential role of bioenergy and facilitate new research.

Rose, Steven K.↗

DOE OSTI2026

Measuring labor productivity dynamics in U.S. industrial and electric power sectors: a case study (2014–2023)

This study proposes a subsystem methodology for measuring labor productivity in the U.S. industrial and electric power sectors by leveraging public data available between 2014 and 2023. Building on Pasinetti’s framework and subsequent developments, the approach employs Vertically Integrated Sectors (VIS) to account for both direct and indirect productivity effects. The novelty of this work is twofold. First, it enables the estimation of productivity trends over time, providing a robust foundation for empirical analysis. Second, it applies the methodology to a case study of the electric generation sector, highlighting its practical relevance. Using data from the Bureau of Economic Analysis, the Bureau of Labor Statistics, and the Impact Analysis for Planning (IMPLAN) tool, the study reveals significant discrepancies between conventional productivity measures and those derived from the VIS approach. Furthermore, the proposed method aligns with the principles of Integrated Energy Systems by capturing the interrelations among generation, distribution, storage, and consumption. This alignment underscores its utility and applications for energy-related policy and planning. Overall, the findings contribute to more precise labor productivity assessments, supporting informed decision-making and future research. Additionally, the method highlights the importance of considering the whole supply chain, providing interrelated metrics for labor productivity which includes both, direct and indirect effects on the final labor productivity metric. By incorporating intersectoral dependencies, this method offers a more comprehensive and accurate measure of labor productivity compared to traditional metrics.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Holistic Transportation and Energy Modeling

This project advances the development and application of the TEMPO transportation energy systems model to support DOE analysis of evolving mobility futures. Recent work has focused on improving model transparency, performance, and fidelity through a redesigned software architecture and updated technology adoption scenarios aligned with the latest data and assumptions. Ongoing efforts aim to expand TEMPO's ability to assess transportation affordability and consumer decision-making, including vehicle ownership dynamics and distributional impacts across households. These enhancements position TEMPO to better inform policy and R&D decisions by providing more behaviorally realistic and policy-relevant insights into transportation energy use, technology adoption, and system-level outcomes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2018

Engineering Elegant Systems: Postulates, Principles, and Hypotheses of Systems Engineering

Systems engineering integrated both the system and the organizational engineering disciplines to produce an elegant system. The NASA Systems Engineering Research Consortium has developed systems engineering postulates, principles, and hypotheses defining the physical and social aspects of systems engineering. This paper presents an overview of the current revision of this basis for systems engineering. This basis addresses several key aspects of systems engineering including system specific approach, organizational influences, policy and law impacts, application across the system life cycle, the mathematical basis of systems engineering, decision making, clearly distinguishing verification from validation, and system optimization.

Watson, Michael D.↗

NASA NTRS2018

Engineering Elegant Systems: Postulates, Principles, and Hypotheses of Systems Engineering

Watson, Michael D.↗

DOE OSTI2022

Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1981

International space policy and the interests of the United States and developing countries

The paper discusses principles which must be upheld in order to protect U.S. interests in space, the potential impact of the Moon Treaty on these principles, how to satisfy the legitimate needs of developing countries while protecting U.S. interests, and how to enhance the probability of success in future negotiations concerning space. It is suggested that, in order to preserve the rights of its citizens and industry to exploit space, the United States must make a strong effort to help solve the problems of the developing countries, maintain a strong space program, and develop and follow a space policy that reflects the interests of the American people.

Bon, B.↗

DOE OSTI2022

Multirobot Collaborative Pursuit Target Robot by Improved MADDPG

Policy formulation is one of the main problems in multirobot systems, especially in multirobot pursuit-evasion scenarios, where both sparse rewards and random environment changes bring great difficulties to find better strategy. Existing multirobot decision-making methods mostly use environmental rewards to promote robots to complete the target task that cannot achieve good results. This paper proposes a multirobot pursuit method based on improved multiagent deep deterministic policy gradient (MADDPG), which solves the problem of sparse rewards in multirobot pursuit-evasion scenarios by combining the intrinsic reward and the external environment. The state similarity module based on the threshold constraint is as a part of the intrinsic reward signal output by the intrinsic curiosity module, which is used to balance overexploration and insufficient exploration, so that the agent can use the intrinsic reward more effectively to learn better strategies. The simulation experiment results show that the proposed method can improve the reward value of robots and the success rate of the pursuit task significantly. The intuitive change is obviously reflected in the real-time distance between the pursuer and the escapee, the pursuer using the improved algorithm for training can get closer to the escapee more quickly, and the average following distance also decreases.

Zhou, Xiao↗

DOE OSTI2017

Estimating the implied cost of carbon in future scenarios using a CGE model: The Case of Colorado

We develop a state-level computable general equilibrium (CGE) model that reflects the roles of coal, natural gas, wind, solar, and hydroelectricity in supplying electricity, using Colorado as a case study. Also, we focus on the economic impact of implementing Colorado's existing Renewable Portfolio Standard, updated in 2013. This requires that 25% of state generation come from qualifying renewable sources by 2020. We evaluate the policy under a variety of assumptions regarding wind integration costs and assumptions on the persistence of federal subsidies for wind. Specifically, we estimate the implied price of carbon as the carbon price at which a state-level policy would pass a state-level cost-benefit analysis, taking account of estimated greenhouse gas emission reductions and ancillary benefits from corresponding reductions in criteria pollutants. Our findings suggest that without the Production Tax Credit (federal aid), the state policy of mandating renewable power generation (RPS) is costly to state actors, with an implied cost of carbon of about $17 per ton of CO 2 with a 3% discount rate. Federal aid makes the decision between natural gas and wind nearly cost neutral for Colorado.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗