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DOE OSTI2022

Evaluation of Horizon of Viability Optimization Engine for Sustained Power to Critical Infrastructure: Preprint

In the aftermath of increasingly frequent catastrophic events, a typical scenario is Critical Infrastructure (CI) units being supported by available backup sources with a weak power grid that can be intermittent or absent. Such a scenario is significantly challenging in the sense of reliable supply of power to CI units. In this article, an intelligent optimization scheme termed as Horizon of Viability (HoV) engine is developed to guarantee the viability of a sustained reliable supply of power to the CI units over a time-horizon. The proposed HoV engine generates a cost-optimal portfolio of the locally available generation sources and the loads over a time horizon using a mixed-integer convex programming problem. A Controller hardware-in-the-loop (CHIL) platform is developed to evaluate the control performance of the HoV engine. The experimental results corroborates the efficacy in maintaining the viability of the CI units after a grid interruption event. Further, the proposed HoV optimization scheme performs better compared to existing net-load management schemes in the literature.

disaster resiliency↗

DOE OSTI2025

Beyond Price-Taker: Multiscale Optimization of Wind and Battery Integrated Energy Systems

Integrating renewable energy into the electric grid is challenging due to the intermittency and variability of wind and other non-dispatchable resources. Integrated energy systems (IESs) combine multiple energy technologies (e.g., fossil, nuclear, renewables, storage) to reduce costs and improve flexibility and reliability. However, standard techno-economic analysis (TEA) methods often overestimate the benefits of IESs because they fail to account for energy market adjustments. This paper systematically studies the limitations of the prevailing price-taker assumption for TEA and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 30.4%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.

25 ENERGY STORAGE↗

DOE OSTI2025

A Neural Optimizer With Decision-Focused Learning for Optimal Energy Storage Operation

Here, this article introduces a neural optimizer-based framework for optimizing battery energy storage system (BESS) control for grid services, including demand charge and energy cost reduction. By leveraging decision-focused learning (DFL), the proposed framework ensures seamless integration and adaptation, significantly enhancing control performance. A patch time-series transformer is employed for peak load forecasting, incorporating aleatoric uncertainty quantification to account for forecasting uncertainties within the decision-making process. The framework utilizes a solver-in-the-loop approach to generate optimal BESS actions, which are then used to train the neural optimizer-based agent. By co-optimizing both BESS operational modes and output power within the NN, the system achieves improved performance and robustness. After initial training, the forecasting and control models are jointly fine-tuned to account for forecasting errors, further improving decision precision and efficiency through DFL. Case studies are performed to validate the performance of the framework using multiple real-world datasets, demonstrating superior performance in monthly peak load forecasting compared to state-of-the-art models. In addition, the results are compared against existing decision-making approaches. The results demonstrate a reduction in monthly peak forecasting error by approximately 15% across various performance measures and achieve an optimization gap for BESS operation that is about three times smaller compared to existing methods.

Kim, Hyeonjin [Pacific Northwest National Laborato↗

DOE OSTI2016

Contingency Contractor Optimization Phase 3 Sustainment Software Design Document - Contingency Contractor Optimization Tool - Prototype

This document describes the final software design of the Contingency Contractor Optimization Tool - Prototype. Its purpose is to provide the overall architecture of the software and the logic behind this architecture. Documentation for the individual classes is provided in the application Javadoc. The Contingency Contractor Optimization project is intended to address Department of Defense mandates by delivering a centralized strategic planning tool that allows senior decision makers to quickly and accurately assess the impacts, risks, and mitigation strategies associated with utilizing contract support. The Contingency Contractor Optimization Tool - Prototype was developed in Phase 3 of the OSD ATL Contingency Contractor Optimization project to support strategic planning for contingency contractors. The planning tool uses a model to optimize the Total Force mix by minimizing the combined total costs for selected mission scenarios. The model optimizes the match of personnel types (military, DoD civilian, and contractors) and capabilities to meet mission requirements as effectively as possible, based on risk, cost, and other requirements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Contingency Contractor Optimization Phase 3 Extension Quick Start Guide: Contingency Contractor Optimization Tool - Engineering Prototype (Release 2.3)

This Quick Start Guide is an abbreviated version of the Contingency Contractor Optimization Phase 3, User Manual for the Contingency Contractor Optimization Tool engineering prototype. It focuses on providing quick access instructions to the core activities of the two main user roles: Planning Manager and Analyst. Based on an electronic storyboard prototype developed in Phase 2, the Contingency Contractor Optimization Tool engineering prototype was refined in Phase 3 of the OSD ATL Contingency Contractor Optimization to support strategic planning for contingency contractors. The tool uses a model to optimize the total workforce mix by minimizing the combined total costs for the selected mission scenarios. The model will optimize the match of personnel types (military, DoD civilian, and contractors) and capabilities to meet the mission requirements as effectively as possible, based on risk, cost, and other requirements.

42 ENGINEERING↗

DOE OSTI2015

Contingency Contractor Optimization Phase 3 Extension User Manual Contingency: Contractor Optimization Tool - Engineering Prototype (Release 2.3)

This User Manual provides step-by-step instructions on the Contingency Contractor Optimization Tool's major features. Activities are organized by user role. The Contingency Contractor Optimization project is intended to address former Secretary Gates' mandate in a January 2011 memo and DoDI 3020.41 by delivering a centralized strategic planning tool that allows senior decision makers to quickly and accurately assess the impacts, risks, and mitigation strategies associated with utilizing contract support. Based on an electronic storyboard prototype developed in Phase 2, the Contingency Contractor Optimization Tool engineering prototype was refined in Phase 3 of the OSD ATL Contingency Contractor Optimization project to support strategic planning for contingency contractors. The planning tool uses a model to optimize the Total Force mix by minimizing the combined total costs for the selected mission scenarios. The model will optimize the match of personnel groups (military, DoD civilian, and contractors) and capabilities to meet the mission requirements as effectively as possible, based on risk, cost, and other requirements.

42 ENGINEERING↗

DOE OSTI2015

Contingency Contractor Optimization Phase 3 User Manual - Contingency Contractor Optimization Tool Prototype - Release 2.2

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2015

Contingency Contractor Optimization Phase 3 Quick Start Guide Contingency Contractor Optimization Tool Prototype - Release 2.2

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Design optimization of an ethanol heavy-duty engine using design of experiments and bayesian optimization

Diesel-fueled engines still hold a large market share in the medium and heavy-duty transportation sector. However, the increase in fossil fuel prices and the strict emission regulations are leading engine manufacturers to seek cleaner alternatives without a compromise in performance. Alcohol-based fuels, such as ethanol, offer a promising alternative to diesel fuel in meeting regulatory demands. Ethanol provides cleaner combustion and lower levels of soot due to its chemical properties, in particular its lower level of carbon content. In addition, the stoichiometric operating conditions of alcohol fueled engines enable the mitigation of NOx emissions in aftertreatment stage. With the promise of retrofitting diesel engines to run on ethanol to reduce emissions, the thermal efficiency of these engines remains the primary optimization target. In order to find the optimal ethanol-fueled engine design that maximizes the thermal efficiency, a large design space needs to be investigated using engineering tools. In this study, previous research by the authors on optimizing the design of a single-cylinder ethanol-fueled engine was extended to explore the design space for a heavy-duty multi-cylinder engine configuration. A heavy-duty engine setup with multiple operating conditions at different engine speeds and loads were considered. A design optimization analysis was performed to identify the potential designs that maximize the indicated thermal efficiency in an ethanol-fueled compression ignition engine. First, a computational fluid dynamics (CFD) model of the engine was validated using experimental data for four drive cycle points. Using a design of experiments (DoE) approach and a parameterized piston bowl geometry, the model was then exercised to explore the relationship among geometric features of the piston bowl and spray targeting angle and indicated thermal efficiency across all tested operating conditions. After evaluating 165~candidate designs, a piston bowl geometry was identified that yielded an increase between 1.3 to 2.2 percentage points in indicated thermal efficiency for all tested conditions, while satisfying the operational design constraints for peak pressure and maximum pressure rise rate. The increased performance was attributed to enhanced mixing that led to the formation of a more homogeneous distribution of in-cylinder temperature and equivalence ratio, higher combustion temperatures, and shorter combustion duration. Finally, a Bayesian optimization (BOpt) analysis was employed to find the optimal piston bowl geometry with a fixed spray injector angle for one of the operating conditions. Using BOpt, a piston candidate was identified that resulted in a 1.9~percentage point increase in thermal efficiency from the baseline design, yet only required 65\% of the design samples investigated using the DoE approach.

Tekgul, Bulut↗

DOE OSTI2016

A multi-objective optimization approach to optimal sensor location problem in $\mathrm{IGCC}$ power plants

Integrated Gasification Combined Cycle (IGCC) power plants provide a cleaner and more efficient way to obtain energy from coal. In order to operate an IGCC power plant in a safe and stable manner, several input and output process parameters need to be monitored. However, due to economic and operational constraints, it is infeasible to place sensors at every input and output process parameter location. Hence, it becomes important to select the most effective sensor locations which lead to maximum information gain about the plant conditions. Practical issues present in an IGCC power plant, such as harsh physical conditions and variability in process parameters, make the optimal sensor location problem an especially complicated one. Further, sensors can have multiple objectives and they can produce uncertainties due to measurement errors. Here this work considers hybrid hardware and virtual sensing for advanced power systems with multiple objectives. In order to solve this real world large scale problem, we use a novel algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). BONUS works in probability distribution space and avoids sampling for each optimization and derivative calculations iterations. A new algorithm for multi-objective optimization is also developed specifically for problem. The result of this nonlinear stochastic multi-objective problem is the non-dominated, or Pareto set, which provides trade-offs between various objectives like observability, cost, and thermal efficiency. This is the first attempt at solving the problem of optimal sensor deployment in advanced power plants, with consideration of hybrid hardware and virtual sensing and incorporation of uncertainty with multiple objectives.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2018

Optimal distributed generation planning in active distribution networks considering integration of energy storage

Not Available

Li, Yang↗

DOE OSTI2018

Optimal Water-Power Flow Problem: Formulation and Distributed Optimal Solution

This paper formalizes an optimal water-power flow (OWPF) problem to optimize the use of controllable assets across power and water systems while accounting for the couplings between the two infrastructures. Tanks and pumps are optimally managed to satisfy water demand while improving power grid operations; {for the power network, an AC optimal power flow formulation is augmented to accommodate the controllability of water pumps.} Unfortunately, the physics governing the operation of the two infrastructures and coupling constraints lead to a nonconvex (and, in fact, NP-hard) problem; however, after reformulating OWPF as a nonconvex, quadratically-constrained quadratic problem, a feasible point pursuit-successive convex approximation approach is used to identify feasible and optimal solutions. In addition, a distributed solver based on the alternating direction method of multipliers enables water and power operators to pursue individual objectives while respecting the couplings between the two networks. The merits of the proposed approach are demonstrated for the case of a distribution feeder coupled with a municipal water distribution network.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow: Preprint

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

Stochastic robust mathematical programming model for power system optimization

We present a stochastic robust framework for two-stage power system optimization problems with uncertainty. The model optimizes the probabilistic expectation of different worst-case scenarios with different uncertainty sets. In addition, a case study of unit commitment shows the effectiveness of the proposed model and algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Riemannian Optimization Applied to AC Optimal Power Flow

The nonlinear, nonconvex AC optimal power flow problem is of growing importance as the nature of the power grid evolves. This problem can be difficult to solve for interior point methods. However, the advent of optimization algorithms over smooth Riemannian manifolds presents an alternative approach. The nonlinear, nonconvex constraints in the AC power flow problem form an embedded submanifold of Euclidean space. In this paper, the authors explore the performance of Riemannian optimization algorithms for the ACOPF problem where the optimization is performed directly on the AC power flow manifold. This is done by using the Julia programming language and the Julia packages PowerModels.jl and Manopt.jl.

AC optimal power flow↗

DOE OSTI2021

Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization

Here, we develop a ytopt autotuning framework that leverages Bayesian optimization to explore the parameter space search and compare four different supervised learning methods within Bayesian optimization and evaluate their effectiveness. We select six of the most complex PolyBench benchmarks and apply the newly developed LLVM Clang/Polly loop optimization pragmas to the benchmarks to optimize them. We then use the autotuning framework to optimize the pragma parameters to improve their performance. The experimental results show that our autotuning approach outperforms the other compiling methods to provide the smallest execution time for the benchmarks syr2k, 3mm, heat-3d, lu, and covariance with two large datasets in 200 code evaluations for effectively searching the parameter spaces with up to 170,368 different configurations. We find that the Floyd-Warshall benchmark did not benefit from autotuning. To cope with this issue, we provide some compiler option solutions to improve the performance. Then we present loop autotuning without a user's knowledge using a simple mctree autotuning framework to further improve the performance of the Floyd-Warshall benchmark. We also extend the ytopt autotuning framework to tune a deep learning application.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Maximization of the annual energy production of wind power plants by optimization of layout and yaw-based wake control: Maximization of wind plant AEP by optimization of layout and wake control

This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant annual energy production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses.

17 WIND ENERGY↗

DOE OSTI2018

The Optimal Wavelengths for Light Absorption Spectroscopy Measurements Based on Genetic Algorithm–Particle Swarm Optimization

To select the optimal wavelengths in the light extinction spectroscopy measurement, genetic algorithm–particle swarm optimization (GAPSO) based on genetic algorithm (GA) and particle swarm optimization (PSO) is adopted. The change of the optimal wavelength positions in different feature size parameters and distribution parameters is evaluated. Moreover, the Monte Carlo method based on random probability is used to identify the number of optimal wavelengths, and good inversion effects of the particle size distribution are obtained. The method proved to have the advantage of resisting noise. In order to verify the feasibility of the algorithm, spectra with bands ranging from 200 to 1000 nm are computed. Based on this, the measured data of standard particles are used to verify the algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗