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DOE OSTI2022

Market mechanism to enable grid-aware dispatch of Aggregators in radial distribution networks

This paper presents a market-based optimization framework wherein Aggregators can compete for nodal capacity across a distribution feeder and guarantee that allocated flexible capacity cannot cause overloads or congestion. This mechanism, thus, allows Aggregators with allocated capacity to pursue a number of services at the whole-sale market level to maximize revenue of flexible resources. Based on Aggregator bids of capacity (MW) and network access price ($/MW), the distribution system operator (DSO) formulates an optimization problem that prioritizes capacity to the different Aggregators across the network while implicitly considering AC network constraints. This grid-aware allocation is obtained by incorporating a convex inner approximation into the optimization framework that prioritizes hosting capacity to different Aggregators. We adapt concepts from transmission-level capacity market clearing, utility demand charges, and Internet-like bandwidth allocation rules to distribution system operations by incorporating nodal voltage and transformer constraints into the optimization framework. Simulation based results on IEEE distribution networks showcase the effectiveness of the approach.

Nazir, Mohammad Nawaf↗

DOE OSTI2024

Repurposing Inactive Oil and Gas Wells for Energy Storage: Maximizing the Potential Via Optimal Drivetrain Control: Preprint

In recent years, there has been a growing emphasis on utilizing energy storage to enhance grid resilience against disruptive events. While renewable energy supply continues to expand, gravity-based solutions like pumped hydro remain dominant in the commercial space. However, their geographical limitations constrain availability, scalability, and increase costs for co-locating solar and wind energy. An alternative approach proposes repurposing idle oil and gas wells located closer to existing grid infrastructure, offering a promising and cost-effective solution. This paper addresses the optimization and control of a regenerative drive system coupled to an interior permanent magnet synchronous machine in a 300-meter well with a 100-Newton weight. The study employs a dynamic MATLAB/Simulink model to simulate the operation of the electric drivetrain system during storage and discharge operations. The results demonstrate an initial round-trip efficiency of 85.9% for the electrical system alone and identify crucial factors for maximizing efficiency. The optimized operation and control of the electromechanical drivetrain system hold great potential for minimizing the levelized cost of storage while maximizing efficiency and revenue generation.

ENERGY STORAGE,ENGINEERING↗

DOE OSTI2020

Management of Risk and Uncertainty Through Optimized Co-Operation of Transmission Systems and Microgrids With Responsive Loads (Final Report)

The evolution of the power system to the reliable, efficient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. Ambitious national and state-level goals around the decarbonization of electricity relies on the integration of very high levels of renewable resources, most of which are variable and intermittent. The use of demand response is an ideal approach to counterbalance the intermittency of renewable generation and brings the consumer into the spotlight. Though individual consumers are interconnected at the low-voltage distribution system, these resources are typically modeled as variables at the transmission network level. Demand-side participation cannot be leveraged effectively without explicitly including the distribution system dynamics in the optimization-based wholesale market operations. This project grew from a vision for co-optimized interaction of distribution systems, or microgrids, with the high-voltage transmission system. In this framework, microgrids encompass consumers, distributed renewables and storage. The energy management system of the lower voltage system (distribution or microgrid) can also sell (buy) excess (necessary) energy from the transmission system. Until recently, very little research had been conducted on the co-optimization of these two systems due to computational limitations. However, advances in computational capabilities, and the judicious use of decomposition methods and innovative approximation methods for high-dimension dynamic programming made this goal a viable objective for this project, leading to a fundamental shift in the ability to integrate and fully utilize demand-side resources. To this end, the modeling framework developed introduces a novel co-optimization framework, to include the operations of both the transmission and distribution systems (or microgrids) in operational decision making. This framework was used to analyze renewable and distributed generation along with responsive demand and to compare the capability of co-optimized systems to perform with higher levels of variable renewables. An ideal microgrid is defined as an electric entity capable of operating in both interconnected (with the high-voltage grid) and islanded mode. As such, the microgrid should incorporate generating units (traditional units and intermittent) and if needed, exchange power with the high-voltage grid. The interplay between the microgrid and high-voltage grid motivated the development of the co-optimization approach to ensure efficient performance of the interconnected network. Results show that the use of a bi-level optimization approach is an appropriate structure, capable of co-optimizing a transmission system with multiple distribution systems and microgrids. While increasing the number of connected systems provides increasing flexibility for renewables integration this can also the economic benefits to the low-voltage subsystems with each additional system connected. Comparison of a traditional single-level decision structure with the co-optimization approach illustrates a reduction in overall system cost under co-optimization, while specific cost allocations to transmission and distribution systems are changed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2004

Adjoint Formulation for an Embedded-Boundary Cartesian Method

Many problems in aerodynamic design can be characterized by smooth and convex objective functions. This motivates the use of gradient-based algorithms, particularly for problems with a large number of design variables, to efficiently determine optimal shapes and configurations that maximize aerodynamic performance. Accurate and efficient computation of the gradient, however, remains a challenging task. In optimization problems where the number of design variables dominates the number of objectives and flow- dependent constraints, the cost of gradient computations can be significantly reduced by the use of the adjoint method. The problem of aerodynamic optimization using the adjoint method has been analyzed and validated for both structured and unstructured grids. The method has been applied to design problems governed by the potential, Euler, and Navier-Stokes equations and can be subdivided into the continuous and discrete formulations. Giles and Pierce provide a detailed review of both approaches. Most implementations rely on grid-perturbation or mapping procedures during the gradient computation that explicitly couple changes in the surface shape to the volume grid. The solution of the adjoint equation is usually accomplished using the same scheme that solves the governing flow equations. Examples of such code reuse include multistage Runge-Kutta schemes coupled with multigrid, approximate-factorization, line-implicit Gauss-Seidel, and also preconditioned GMRES. The development of the adjoint method for aerodynamic optimization problems on Cartesian grids has been limited. In contrast to implementations on structured and unstructured grids, Cartesian grid methods decouple the surface discretization from the volume grid. This feature makes Cartesian methods well suited for the automated analysis of complex geometry problems, and consequently a promising approach to aerodynamic optimization. Melvin e t al. developed an adjoint formulation for the TRANAIR code, which is based on the full-potential equation with viscous corrections. More recently, Dadone and Grossman presented an adjoint formulation for the Euler equations. In both approaches, a boundary condition is introduced to approximate the effects of the evolving surface shape that results in accurate gradient computation.

Nemec, Marian↗

DOE OSTI2024

Optimization-Based Model Reduction Scheme for Renewable Energy Power Plants Using Standardized Testing Scenarios

This paper presents an optimization-based model reduction scheme for renewable energy (RE) power plants consisting of inverter-based resources (IBRs) operating in grid-following (GFL) or grid-forming (GFM) modes. More importantly, the datasets feeding the optimization-based model reduction scheme are generated and re-used through the standardized grid-interactive testing scenarios. Particularly, the proposed scheme makes use of the power plant point of common coupling (PCC) measurements of various quantities specified by standardized tests (e.g., voltage and frequency ride through) as per IEEE 2800, to estimate the parameters of the reduced-order model such that its dynamic performance aligns with the original detailed power plant model. The proposed model reduction approach does not require the parameters of individual IBRs and using standardized test data as input to the formulated optimization problem simplifies the reduced-order modelling scheme. Extensive case studies following standardized test scenarios verified the remarkable accuracy of the proposed approach.

Yallamilli, Ram S. [Purdue University]↗

DOE OSTI2021

Coordinated Inverter Control to Increase Dynamic PV Hosting Capacity: A Real-Time Optimal Power Flow Approach

High penetrations of distributed photovoltaics (PV) could cause adverse grid impacts, such as voltage violations. The recent development in inverter technologies provides the opportunity to develop control systems to realize effective PV governance and thus to improve dynamic PV hosting capacity for distribution grids. In this paper, a novel distributed energy resource management system (DERMS) solution is proposed by adopting the real-time optimal power flow (OPF) approach for coordinated control of the distributed PV inverters. The proposed approach eliminates the dependence on load knowledge via measurement feedback correction, and it can be implemented in real time. One challenge is that the technique is sensitive to the data availability and integrity of voltage measurements. Therefore, a decentralized DERMS approach is developed by leveraging the concepts of state estimation. The framework and the effectiveness of the solution approach are numerically demonstrated on a real distribution feeder in Southern California.

14 SOLAR ENERGY↗

DOE OSTI2021

Optimal interpolation of global dissolved oxygen: 1965–2015

Oxygen inventory of the global ocean has declined in recent decades potentially due to the warming-induced reduction in solubility as well as the circulation and biogeochemical changes associated with ocean warming and increasing stratification. Earth System Models predict continued oxygen decline for this century with profound impacts on marine ecosystem and fisheries. Observational constraint on the rate of oxygen loss is crucial for assessing the ability of models to accurately simulate these changes. There are only a few observational assessments of the global oceanic oxygen inventory reporting a range of oxygen loss. This study develops a gridded data set of dissolved oxygen for the global oceans using optimal interpolation method. The resulting gridded product includes full-depth map of dissolved oxygen as 5-year moving average from 1965 to 2015 with uncertainty estimates. The uncertainty can come from unresolved small-scale and high-frequency variability and mapping errors. The multi-decadal trend of global dissolved oxygen is in the range of -281 to -373 Tmol/decade. This estimate is more conservative than previous works. In this study, the grid points far from the observations are essentially set equal to zero anomaly from the climatology. Calculating global inventory with this approach produces a relatively conservative estimate; thus, the results from this study likely provide a useful lower bound estimate of the global oxygen loss.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2004

Aerodynamic Shape Optimization Based on Free-form Deformation

This paper presents a free-form deformation technique suitable for aerodynamic shape optimization. Because the proposed technique is independent of grid topology, we can treat structured and unstructured computational fluid dynamics grids in the same manner. The proposed technique is an alternative shape parameterization technique to a trivariate volume technique. It retains the flexibility and freedom of trivariate volumes for CFD shape optimization, but it uses a bivariate surface representation. This reduces the number of design variables by an order of magnitude, and it provides much better control for surface shape changes. The proposed technique is simple, compact, and efficient. The analytical sensitivity derivatives are independent of the design variables and are easily computed for use in a gradient-based optimization. The paper includes the complete formulation and aerodynamics shape optimization results.

Samareh, Jamshid A.↗

DOE OSTI2024

Empowering Rural Electrification in Honduras: An Integrated Assessment of PV/BESS and Productive Uses of Electricity in Gracias a Dios

Honduras faces significant challenges in its energy sector, particularly in rural areas where access to reliable and affordable electricity remains limited. The flagship rural electrification initiative for Honduras Secretary of Energy (SEN) is the Politica de Acceso Universal a la Electricidad (PAUEH - Universal Electricity Access Policy), a key solution for addressing this energy poverty is the deployment of more than 1700 distributed solar and hybrid mini-grid solutions. In late 2023 as a first step towards supporting SEN's electrification efforts, the National Renewable Energy Laboratory (NREL) developed a literature review of SEN electrification policy documents and conducted a series of technical capacity-building workshops with SEN and other energy sector stakeholders in Honduras focused on using NREL's open-source REopt tool to conduct techno-economic assessments and develop least-cost optimizations for potential solar + storage mini-grid systems. Building on some initial capacity building on mini-grid modeling, the National Renewable Energy Laboratory (NREL) worked with SEN to develop a detailed techno-economic assessment for electrifying two schools, a healthcare clinic, and a hospital in a hypothetical community within the department of Gracias a Dios. The analysis also evaluates the business case for cold storage productive use of energy (PUE) applications for the fisheries value chain and how the incorporation of these PUE loads potentially impacts both the viability of the PV+BESS solutions as well as local economic development. By assessing the potential for deployment of integrated PV/BESS systems to both support critical community services like education and healthcare, as well as potential for downstream enterprise and economic development, this analysis represents a first step that can help to inform specific strategies for development of pilot PV+BESS projects aligned with national priorities and sector level planning under PAUEH.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Fortalecimiento de la Electrificación Rural en Honduras: Una Evaluación Integrada de PV+BESS y Usos Productivos de la Electricidad en Gracias a Dios, Honduras [Empowering Rural Electrification in Honduras: An Integrated Assessment of PV/BESS and Productive Uses of Electricity in Gracias a Dios] (Spanish Translation)

14 SOLAR ENERGY↗

NASA NTRS1977

Atmospheric model development in support of SEASAT. Volume 1: Summary of findings

Atmospheric analysis and prediction models of varying (grid) resolution were developed. The models were tested using real observational data for the purpose of assessing the impact of grid resolution on short range numerical weather prediction. The discretionary model procedures were examined so that the computational viability of SEASAT data might be enhanced during the conduct of (future) sensitivity tests. The analysis effort covers: (1) examining the procedures for allowing data to influence the analysis; (2) examining the effects of varying the weights in the analysis procedure; (3) testing and implementing procedures for solving the minimization equation in an optimal way; (4) describing the impact of grid resolution on analysis; and (5) devising and implementing numerous practical solutions to analysis problems, generally.

Kesel, P. G.↗

DOE OSTI2021

Coordinated Optimization and Control of Residential Solid State Power Substations in Electrical Distribution Network

This paper proposes a computationally efficient mixed integer linear programming (MILP) model for the coordinated optimization of solid-state power substations (SSPSs) in a feeder considering the full unbalanced three-phase structure of the distribution grid and its characteristics. The proposed model determines the optimal real and reactive power of each SSPS at the point of common coupling (PCC) that minimizes the operating cost and maximizes the system performance, e.g., voltage regulation and phase balancing. To improve the computational efficiency, an inscribed octagon is introduced to approximate the quadratic capacity constraints of components. Numerical simulation results show the effectiveness of the proposed model and significant improvements in voltage profiles and power imbalance between phases.

Liu, Guodong↗

DOE OSTI2019

Bayesian-based Hyperparameter Optimization for Spiking Neuromorphic Systems

Designing a neuromorphic computing system involves selection of several hyperparameters that not only affect the accuracy of the framework, but also the energy efficiency and speed of inference and training. These hyperparameters might be inherent to the training of the spiking neural network (SNN), the input/output encoding of the real-world data to spikes, or the underlying neuromorphic hardware. In this work, we present a Bayesian-based hyperparameter optimization approach for spiking neuromorphic systems, and we show how this optimization framework can lead to significant improvement in designing accurate neuromorphic computing systems. In particular, we show that this hyperparameter optimization approach can discover the same optimal hyperparameter set for input encoding as a grid search, but with far fewer evaluations and far less time. We also show the impact of hardware-specific hyperparameters on the performance of the system, and we demonstrate that by optimizing these hyperparameters, we can achieve significantly better application performance.

Parsa, Maryam↗

NASA NTRS2003

An Exact Dual Adjoint Solution Method for Turbulent Flows on Unstructured Grids

An algorithm for solving the discrete adjoint system based on an unstructured-grid discretization of the Navier-Stokes equations is presented. The method is constructed such that an adjoint solution exactly dual to a direct differentiation approach is recovered at each time step, yielding a convergence rate which is asymptotically equivalent to that of the primal system. The new approach is implemented within a three-dimensional unstructured-grid framework and results are presented for inviscid, laminar, and turbulent flows. Improvements to the baseline solution algorithm, such as line-implicit relaxation and a tight coupling of the turbulence model, are also presented. By storing nearest-neighbor terms in the residual computation, the dual scheme is computationally efficient, while requiring twice the memory of the flow solution. The scheme is expected to have a broad impact on computational problems related to design optimization as well as error estimation and grid adaptation efforts.

Nielsen, Eric J.↗

DOE OSTI2023

Optimizing mixed cool thermal storage systems across a connected community

A high level of electric demand flexibility must be integrated into our building infrastructure to enable greater renewable energy penetration in the grid. In the U.S., 9% of electricity generated is used to cool buildings in a periodic manner, making this end-use an ideal target for active management through cool thermal energy storage (CTES) technologies. Historic uses for CTES are designed around central chilled water plants, but these systems cool less than 25% of U.S. commercial floorspace. Emerging technologies are under development to serve the many smaller distributed cooling systems, such as rooftop units (RTUs), and have the potential to add CTES to an additional 66% of cooled commercial floorspace. However, these unitary thermal storage systems (UTSS) lack the modeling and analysis tools to evaluate them in the future interactive grid context. Here, this study develops the modeling and optimization tools necessary to simultaneously examine central and distributed ice storage systems within the multi-building, connected community context. An integrated simulation-optimization workflow is created to allow for rapid customized analysis. Results demonstrate the energy and flexibility tradeoffs of various implementations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Local Feature Sufficiency Exploration for Predicting Security-constrained Generation Dispatch in Multi-Area Power Systems

Deriving generation dispatch is essential for efficient and secure operation of electric power systems. This is usually achieved by solving a security-constrained optimal power flow(SCOPF) problem, which is by nature non-convex, nonlinear and thus computational intensive. The state-of-the-art optimization approaches are not able to solve this problem for large-scale power systems within power system operation time window (usually 5 minutes). In this work, we developed supervised learning approaches to determine security-constrained generation dispatch within much shorter time window. More importantly, the physical constrain of only accessing to local measurements and other information in most utilities’ real-time operation can not be ignored for the predictive models.The feasibility and accuracy of utilizing only local features(measurements and grid information in one area) to predict optimal local generation dispatch (dispatch of all generators in the corresponding area) in multi-area power systems has been explored. The results showed optimal local generation dispatch can be predicted with local features with high accuracy, which is comparable to the results obtained with global features.

Sun, Yixuan↗

DOE OSTI2025

Distributed Control and Optimization of Virtual Inertia in Power Systems

The integration of renewable energy sources into power grids through inverter-based resources (IBRs) reduces system inertia, leading to faster frequency dynamics and potential grid instability. This paper proposes a distributed, consensus-based approach for the real-time control and optimization of inertia sources during system disturbances, enhancing both system stability and economic performance. Furthermore, we integrate this approach with the traditional inertia placement problem, demonstrating its suitability for existing frameworks. We validate its effectiveness in a 4-bus test system and a two-area 8-bus test system, showcasing its potential for scalable, dynamic inertia management in low-inertia grids.

Yadav, Ajay [ORNL] (ORCID:000000016111881X)↗

NASA NTRS2015

Aerodynamic Shape Optimization of a Dual-Stream Supersonic Plug Nozzle

Aerodynamic shape optimization was performed on an isolated axisymmetric plug nozzle sized for a supersonic business jet. The dual-stream concept was tailored to attenuate nearfield pressure disturbances without compromising nozzle performance. Adjoint-based anisotropic mesh refinement was applied to resolve nearfield compression and expansion features in the baseline viscous grid. Deformed versions of the adapted grid were used for subsequent adjoint-driven shape optimization. For design, a nonlinear gradient-based optimizer was coupled to the discrete adjoint formulation of the Reynolds-averaged Navier- Stokes equations. All nozzle surfaces were parameterized using 3rd order B-spline interpolants and perturbed axisymmetrically via free-form deformation. Geometry deformations were performed using 20 design variables shared between the outer cowl, shroud and centerbody nozzle surfaces. Interior volume grid deformation during design was accomplished using linear elastic mesh morphing. The nozzle optimization was performed at a design cruise speed of Mach 1.6, assuming core and bypass pressure ratios of 6.19 and 3.24, respectively. Ambient flight conditions at design were commensurate with 45,000-ft standard day atmosphere.

shape optimization↗