Search NASA⌕ Search

SEARCH · Search NASA

Results for “Policy optimization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This study develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach to address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. Finally, the proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.

09 BIOMASS FUELS↗
DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1980

Modal cost analysis for linear matrix-second-order systems

Reduced models and reduced controllers for systems governed by matrix-second-order differential equations are obtained by retaining those modes which make the largest contributions to quadratic control objectives. Such contributions, expressed in terms of modal data, used as mode truncation criteria, allow the statement of the specific control objectives to influence the early model reduction from very high order models which are available, for example, from finite element methods. The relative importance of damping, frequency, and eigenvector in the mode truncation decisions are made explicit for each of these control objectives: attitude control, vibration suppression and figure control. The paper also shows that using modal cost analysis (MCA) on the closed loop modes of the optimally controlled system allows the construction of reduced control policies which feedback only those closed loop modal coordinates which are most critical to the quadratic control performance criterion. In this way, the modes which should be controlled (and hence the modes which must be observable by choice of measurements), are deduced from truncations of the optimal controller.

Skelton, R. E.↗
DOE OSTI2023

Beyond Price Taker: Conceptual Design and Optimization of Integrated Energy Systems Using Machine Learning Market Surrogates

Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
DOE OSTI2024

Convex Relaxations of Maximal Load Delivery for Multi-Contingency Analysis of Joint Electric Power and Natural Gas Transmission Networks

Recent increases in gas-fired power generation have engendered increased interdependencies between natural gas and power transmission systems. These interdependencies have amplified existing vulnerabilities in gas and power grids, where disruptions can require the curtailment of load in one or both systems. Although typically operated independently, coordination of these systems during severe disruptions can allow for targeted delivery to lifeline services, including gas delivery for residential heating and power delivery for critical facilities. To address the challenge of estimating maximum joint network capacities under such disruptions, we consider the task of determining feasible steady-state operating points for severely damaged systems while ensuring the maximal delivery of gas and power loads simultaneously, represented mathematically as the nonconvex joint Maximal Load Delivery (MLD) problem. To increase its tractability, we present a mixed-integer convex relaxation of the MLD problem. Then, to demonstrate the relaxation’s effectiveness in determining bounds on network capacities, exact and relaxed MLD formulations are compared across various multi-contingency scenarios on nine joint networks ranging in size from 25 to 1191 nodes. The relaxation-based methodology is observed to accurately and efficiently estimate the impacts of severe joint network disruptions, often converging to the relaxed MLD problem’s globally optimal solution within ten seconds.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Federated Deep Reinforcement Learning for Decentralized VVO of BTM DERs

The future of grid control requires a hybrid approach combining centralized and decentralized methods to fully utilize the potential of smart edge devices with artificial intelligence (AI) capabilities. This paper aims to develop and evaluate a federated deep reinforcement learning (FDRL) framework for decentralized adaptive volt-var optimization (VVO) of behind-the-meter (BTM) distributed energy resources (DERs). First, this paper models a single deep reinforcement learning (DRL) agent using the Markov Decision Process (MDP) framework for decentralized adaptive VVO of BTM DERs. Two DRL algorithms, soft actor-critic (SAC) and twin-delayed deep deterministic policy gradient (TD3), are compared for their effectiveness in optimizing VVO. Results show that TD3 outperforms SAC, achieving a 71.3% improvement in mean reward. Finally, the DRL agent is deployed within the FDRL framework, using the Flower platform, to enhance learning, provide adaptive control, and ensure data privacy for BTM DERs.

Ravi, Abhijith↗
DOE OSTI2023

Calibrating Constant Elasticity of Substitution Technologies to Bottom-up Cost Estimates

We propose a method for calibrating an industry-level technology to engineering (bottom-up) estimates with a particular focus on abatement opportunities. As a demonstration, substitution elasticities across inputs are adjusted in the nested cost function for the electricity sector to best fit a target marginal abatement cost (MAC) curve derived from engineering assessments of available technologies. Elasticities are optimized over an entire relevant range of the MAC, whereas current techniques use local point estimates under little or no abatement. In the context of fitting to a given MAC we evaluate alternative nesting structures and find that, while complexity in nesting improves the fit, even relatively simple nesting structures can reasonably approximate the target MAC. In our example, focused on the electricity sector, we find standard elasticities adopted in top-down models moderately overstate abatement costs relative to the engineering targets. In our preferred specification the most important adjustment is to escalate the substitution elasticity between energy and value-added inputs. This is consistent with an argument that the current set of point estimates fail to properly account for new capital-based technologies. These conclusions, however, are sensitive to our assumption about output-intensity abatement and consumer price responsiveness, both of which are not delineated in engineering estimates.

abatement cost↗
DOE OSTI2020

Multi-Stage and Multi-Timescale Robust Co-Optimization Planning for Reliable and Sustainable Power Systems. Final Report

In this project, Clarkson University, in collaboration with Southern Methodist University and University of Pittsburgh, conducted the research to model, design, and implement a sophisticated generation and transmission co-optimization planning decision tool, called Multi-stage and Multi-timescale robust Co-Optimization Planning (MMCOP). The MMCOP decision tool intends to facilitate generation and transmission co-optimization planning of emerging power systems, while mitigating risks and uncertainties in both short-term operation dynamics and long-term policy and technology changes. Long-term power system planning aims at optimizing asset utilization by investing in a proper mix of various generation technologies and transmission lines to supply the future load growth. In particular, the Clean Power Plan (CPP), which is designed to combat climate change and reduce carbon emissions by setting a national limit on carbon pollution from power plants, may dramatically change the landscape of the power industry by further promoting clean energy and phasing out emissions-intensive generation technologies. In addition, novel non-wire alternatives (e.g., demand response (DR), distributed generation (DG), energy efficiency (EE), and smart grid technologies) and the computational complexity for large-scale systems significantly complicate the system planning procedure even further. However, existing conventional planning approaches neglect short-term variability and uncertainty of renewable energy, hourly chronological operation details, and physical nonlinear characteristics of the alternating current transmission network. As a result, existing conventional planning approaches may not work properly, and power systems reliability could be in jeopardy. In observing the limitations of existing conventional planning approaches and addressing new challenges of emerging power systems, the main scope of this project to develop co-optimization planning models within a multi-stage and multi-timescale framework. In particular, random contingencies, key uncertainty factors, and AC power flows are included to derive expansion plans while considering both long-term reliability and short-term flexibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Probabilistic Deliverability Assessment of Distributed Energy Resources via Scenario-Based AC Optimal Power Flow

As electric grids decarbonize and distributed energy resources (DERs) become increasingly prevalent, interconnection assessments must evolve to reflect operational variability and control flexibility. This paper highlights key modeling limitations observed in practice and reviews approaches for modeling uncertainty. It then introduces a Probabilistic Deliverability Assessment (PDA) framework designed to complement and extend existing procedures. The framework integrates scenario-based AC optimal power flow (AC OPF), corrective dispatch, and optional multi-temporal constraints. Together, these form a structured methodology for quantifying DER utilization, deliverability, and reliability under uncertainty in load, generation, and topology. Outputs include interpretable metrics with confidence intervals that inform siting decisions and evaluate compliance with reliability thresholds across sampled operating conditions. A case study on Puerto Rico’s publicly available bulk power system model demonstrates the framework’s application using minimal input data, consistent with current interconnection practice. Across staged fossil generation retirements, the PDA identifies high-value DER sites and regions requiring additional reactive power support. Results are presented through mean dispatch signals, reliability metrics, and geospatial visualizations, demonstrating how the framework provides transparent, data-driven siting recommendations. The framework’s modular design supports incremental adoption within existing workflows, encouraging broader use of AC OPF in interconnection and planning contexts.

14 SOLAR ENERGY↗
DOE OSTI2017

Optimal management program for asbestos containing building materials to be available in the event of a disaster

Highlights: • The safe management of asbestos is a matter of considerable importance. • There is a lack of literature concerning the development of a program for ACBMs. •A management program for the optimal disposal of ACBMs has been developed. • This program is also available in the event of a post-disaster. - Abstract: The safe management and disposal of asbestos is a matter of considerable importance. A large number of studies have been undertaken to quantify the issue of waste management following a disaster. Nevertheless, there have been few (if any) studies concerning asbestos waste, covering the amount generated, the cost of disposal, and the degree of hazard incurred. Thus, the current study focuses on developing a program for the management of Asbestos Containing Building Materials (ACBMs), which form the source of asbestos waste in the event of a disaster. The study will also discuss a case study undertaken in a specific region in Korea in terms of: (1) the location of ACBM-containing buildings; (2) types and quantities of ACBMs; (3) the cost of ACBM disposal; (4) the amount of asbestos fiber present during normal times and during post-disaster periods; (5) the required order in which ACBM-containing buildings should be dismantled; and (6) additional greenhouse gases generated during ACBM removal. The case study will focus on a specific building, with an area of 35.34 m{sup 2}, and will analyze information concerning the abovementioned points. In addition, the case study will focus on a selected area (108 buildings) and the administrative district (21,063 buildings). The significance of the program can be established by the fact that it visibly transmits information concerning ACBM management. It is a highly promising program, with a widespread application for the safe management and optimal disposal of asbestos in terms of technology, policy, and methodology.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Quantifying Energy Justice Goals in the Power Sector: Developing and Using Metrics

New policy goals are explicitly guiding the future grid toward greater energy equity. At the same time, policy goals also guide the grid toward decarbonization and resilience, while maintaining cost, security, and reliability cornerstone requirements. In conclusion, these pressures create a dilemma: moving urgently to address climate change and respond to energy disruptions, but also slowly to engage communities on the climate frontlines in earnest.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2024

Development of A Composite Drought Indicator for Operational Drought Monitoring in the MENA Region

This paper presents the composite drought indicator (CDI) that Jordanian, Lebanese, Moroccan, and Tunisian government agencies now produce monthly to support operational drought management decision making, and it describes their iterative co-development processes. The CDI is primarily intended to monitor agricultural and ecological drought on a seasonal time scale. It uses remote sensing and modelled data inputs, and it reflects anomalies in precipitation, vegetation, soil moisture, and evapotranspiration. Following quantitative and qualitative validation assessments, engagements with policymakers, and consideration of agencies’ technical and institutional capabilities and constraints, we made changes to CDI input data, modelling procedures, and integration to tailor the system for each national context. We summarize validation results, drought modelling challenges and how we overcame them through CDI improvements, and we describe the monthly CDI production process and outputs. Finally, we synthesize procedural and technical aspects of CDI development and reflect on the constraints we faced as well as trade-offs made to optimize the CDI for operational monitoring to support policy decision-making—including aspects of salience, credibility, and legitimacy—within each national context.

Karim Bergaoui↗
NASA NTRS2024

Cost and Benefit Analysis of Mitigating, Tracking, and Remediating Orbital Debris

Orbital debris may collide with crewed and robotic spacecraft, placing them at risk. The wide range of debris, from 9,000-kilogram rocket bodies to millions of millimeter-size debris, has led to a similarly wide range of proposed actions for addressing the risks posed by debris. However, the costs and benefits of these actions have historically been unknown. This is a challenge for decision makers who are choosing which actions to support through technology development or policy changes. NASA’s Office of Technology, Policy, and Strategy is addressing these technical and economic uncertainties by building a capability to (1) complete rigorous calculations of the net present value of each action, (2) identify an optimal portfolio of actions to reduce risk, and (3) quantitatively analyze policies related to space sustainability. This report describes our progress toward that capability and to solicit feedback from the space and economic communities. Our previous work, referred to here as Phase 1, assessed the costs and benefits of performing debris remediation on operationally relevant timescales. The current analysis contains major updates to the risk model used in Phase 1 and expands the breadth of actions considered to include mitigating the creation of debris, improving the ability to track debris, and more methods for cleaning up existing debris. We demonstrate that our approach of measuring risks in dollars allows for the effectiveness of seemingly incommensurate actions to be compared and generates insights that other approaches to measuring risk have missed.

Jericho Locke↗
NASA NTRS2024

Cost and Benefit Analysis of Mitigating, Tracking, and Remediating Orbital Debris

Presentation accompanying the technical report with the same name. The abstract for the report and the full study is repeated below. Orbital debris may collide with crewed and robotic spacecraft, placing them at risk. The wide range of debris, from 9,000-kilogram rocket bodies to millions of millimeter-size debris, has led to a similarly wide range of proposed actions for addressing the risks posed by debris. However, the costs and benefits of these actions have historically been unknown. This is a challenge for decision makers who are choosing which actions to support through technology development or policy changes. NASA’s Office of Technology, Policy, and Strategy is addressing these technical and economic uncertainties by building a capability to (1) complete rigorous calculations of the net present value of each action, (2) identify an optimal portfolio of actions to reduce risk, and (3) quantitatively analyze policies related to space sustainability. This report describes our progress toward that capability and to solicit feedback from the space and economic communities. Our previous work, referred to here as Phase 1, assessed the costs and benefits of performing debris remediation on operationally relevant timescales. The current analysis contains major updates to the risk model used in Phase 1 and expands the breadth of actions considered to include mitigating the creation of debris, improving the ability to track debris, and more methods for cleaning up existing debris. We demonstrate that our approach of measuring risks in dollars allows for the effectiveness of seemingly incommensurate actions to be compared and generates insights that other approaches to measuring risk have missed.

Jericho Locke↗
NASA NTRSDate not supplied

A Cost and Benefit Analysis of Orbital Debris Remediation, Mitigation,Tracking, and Characterization

Orbital debris may collide with crewed and robotic spacecraft, placing them at risk. The wide range of debris, from 9,000-kilogram rocket bodies to millions of millimeter-size debris, has led to a similarly wide range of proposed actions for addressing the risks posed by debris. However, the costs and benefits of these actions have historically been unknown. This is a challenge for decision makers who are choosing which actions to support through technology development or policy changes. NASA’s Office of Technology, Policy, and Strategy is addressing these technical and economic uncertainties by building a capability to (1) complete rigorous calculations of the net present value of each action, (2) identify an optimal portfolio of actions to reduce risk, and (3) quantitatively analyze policies related to space sustainability. This report describes our progress toward that capability and to solicit feedback from the space and economic communities. Our previous work(Colvin, Karcz, and Wusk. 2023), referred to here as Phase 1, assessed the costs and benefits of performing debris remediation on operationally relevant timescales. The current paper summarizes the work of Locke and Colvin (2024), which contains major updates to the risk model used in Phase 1 and expands the breadth of actions considered to include mitigating the creation of debris, improving the ability to track debris, and more methods for cleaning up existing debris. We demonstrate that our approach of measuring risks in dollars allows for the effectiveness of seemingly incommensurate actions to be compared and generates insights that other approaches to measuring risk have missed.

Orbital Debris↗
DOE OSTI2024

Scale-Bridging Optimization Framework for Desalination Integrated Produced Water Networks

In this work, we develop a Pyomo-based non-linear optimization strategy that includes rigorous MVR models. The detailed desalination unit is integrated into the multiperiod produced water network problem using the trust region filter (TRF) method. TRF decomposes the integrated problem into a master problem consisting of the network variables and a simplified surrogate model for the detailed desalination unit. The surrogate is updated using zero and first-order corrections from the optimal solution of the detailed models at every iteration. This framework allows us to co-optimize the design of the desalination units and operating policy for the multiperiod network. A common design is ensured across all periods using global capacity constraints. We validate the solution obtained using the TRF method by solving the full integrated problem for small network instances and show our results on real case studies on produced water networks from the Permian and Appalachian basins. In this work, we describe our TRF formulation, give details on our implementation in Pyomo, and analyze the results obtained by solving the optimization problem using IPOPT. We also present a discussion on the computational efficiency and scaling using the TRF approach against a full-scale integration of the rigorous models within the water network.

Naik, Sakshi↗
DOE OSTI2022

Optimal Gas-Electric Energy System Decarbonization Planning

As energy utilities implement climate change mitigation policies, system planners require strategies for achieving affordable emissions reductions. Coordinated planning of electric power and natural gas systems will allow synergistic investments to address cross-sector operational constraints, competing uses for net-zero emissions fuels, and shifts in energy demands across energy carriers. In this study, we develop a novel optimization program that finds the cost-minimizing mix of infrastructure expansion or reduction across gas and electric systems to satisfy sector-specific emissions constraints. Alongside energy supply resources, our framework allows for central-planning of end-use equipment stocks to allow switching between gas and electric appliances upon failure or premature replacement. The proposed model is used to simulate case study scenarios for a benchmark 24-pipe gas network coupled to a 24-node power system test network. We find that electrification of greater than 80% of core gas demands is a component of the least-cost solution for modeled energy systems. Despite this substitution, the gas system is maintained to service difficult-to-electrify customers and to deliver net-zero emissions gas to electricity generators in times of peak electricity demand. Restricting electrification of gas appliances increases reliance on power- to-gas technologies and increases annual costs by 15% in 2040. Neglecting constraints on pipeline blending of hydrogen can produce a misleading result that relies on hydrogen blend fractions of greater than 50%. In all cases, we find the average costs of delivered gas increase nearly 5-fold across the decarbonization transition, highlighting the importance of future work investigating cost-allocation strategies for ensuring an equitable energy transition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗