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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.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Optimizing Fleet Operations With Telematics [Slides]

Through the U.S. Department of Energy's Energy to Communities (E2C) program, NLR, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Prince William County, VA with information about fleet charging and telematics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Control architecture for predictive and optimal vehicle operations in a single vehicle environment

There is disclosed a cloud computing control system for vehicle speed control and also for control of a vehicle in a platoon. The cloud computing control system determines a speed trajectory and neutral coasting command for a first vehicle of the platoon and a vehicle controller determines a reference speed for the first vehicle in response to the speed trajectory and the neutral coasting command, and is response to one or more vehicle specific factors associated with the first vehicle.

Borhan, Hoseinali↗
DOE OSTI2022

Case Study: Hybridizing Nuclear Energy Systems in the U.S.

This case study was presented at the ICTP-IAEA VIRTUAL Course on Nuclear-Renewable Integrated Energy Systems: Phenomenology, Research and Development. It provides preliminary results on an analysis of hybridizing the two nuclear power plants owned by Xcel Energy and located in Minnesota. The analysis extends previous analyses that provided only price-taker results. Those assume adjusting the generation sold to the grid does not impact locational marginal electricity prices nor do they estimate impacts on the total net cost to serve the load. This presentation summarizes the new technique that was developed to both optimize the size and operations of the hybridized system and the resulting impacts on the grid when it is operated optimally.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A trilevel model against false gas-supply information attacks in electricity systems

The interdependence between natural gas and electricity systems is increasing rapidly due to the growing reliance on natural gas-fired generating units. Availability of natural gas for gas-fired generating units can impact the secure operation of electricity systems. Fuel supply shortage for gas-fired units can be caused by uncertain interruptible supply contracts and incorrect supply information. This article proposes a trilevel min-max-min defender-attacker-operator optimization problem to provide the power system operator a screening methodology that allocates a limited budget for best protecting critical fuel supply information, and also the strategies to sign firm supply contract to reduce natural gas supply uncertainties. We utilize a column and constraint generation (C&CG) algorithm to solve the proposed problem. We illustrate the effectiveness of this trilevel formulation using a case study based on IEEE 24-node test system.

42 ENGINEERING↗
DOE OSTI2024

Networked Microgrids Optimization

This project is mainly about the operation optimization of three networked microgrids (MG), including centralized optimization and distributed optimization. The alternating direction method of multipliers (ADMM) algorithm is used for distributed optimization. In the distribution network considered here, there is a Distribution Management system (DMS) as the system coordinator and several networked microgrids. In grid-connected mode, power could be imported or exported at the distribution substation bus according to the utility rate, and the exchanged power at point of common coupling (PCC) of any microgrid has a limitation. In islanded mode, the power imports/exports at the distribution substation are zero. In both grid-connected and islanded mode, the distribution substation is taken as a slack bus with fixed voltage magnitude.

Chen, Yang [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2022

GOOML - Real World Applications of Machine Learning in Geothermal Operations

GOOML (Geothermal Operational Optimization with Machine Learning) is a machine-learning based framework that enables geothermal power plant operators to explore optimization opportunities for their assets in an efficient and robust digital environment. Backed by real-world data sources, thermodynamic constraints and steamfield intelligence, the GOOML environment provides new tools to explore how to best operate steamfields as well as test new scenarios and configurations prior to implementation in the field. To prove the effectiveness of GOOML, we have undertaken optimization experiments using reinforcement learning (RL) to generate operational suggestions using a balance of mass-take targets, sustainability considerations and net generation. Our experiments use the GOOML construct to explore different field parameters and perform multiple reinforcement learning experiments. Like a comprehensive laboratory workbench, we can change out components of a steamfield to perform testing under a variety of conditions (restrict mass, increase pressure, reroute steam, etc.). This flexibility allows us to explore conditions that would require significant infrastructure changes in a real-world setting at a fraction of the cost and time in a digital environment. The results highlight the benefits of using digital twins and advanced data analytics for the geothermal industry.

forecasting↗
DOE OSTI2020

Hylia: Time-Series Library for Network Operations (Hylia) v1

The ability to analyze time-series data in network operations is critical to ensure optimal operations and management in wide area networks, sensor networks, and cloud networks. Hylia is a collection of multiple time-series prediction algorithms designed specifically to work with network monitoring data sets such as SNMP, NETFLOW and other monitoring data. It is designed to recognize these features and perform the extrapolation of the data.

Kiran, Mariam↗
DOE OSTI2021

Optimal toolpath generation system and method for additively manufactured composite materials

The present disclosure relates to a system for optimizing an additive manufacturing (AM) process. The system may use a printing component for using a material to form a component in a layer-by-layer process. An electronic controller may control movement of one of the printing component or a substrate on which the component is being formed, in a manner to optimize a toolpath for the printing component as each layer of the component is formed. Optimization operations are performed using an optimization subsystem which enables manufacturability constraints, as well as optimized toolpaths for each layer of the component, to be defined using contours of a level-set function. The level-set function may be used to define the optimized toolpaths within a fixed, predetermined grid, and the optimized toolpaths then used to generate suitable code for controlling movement of the printing component relative to the substrate to create the part or structure in a layer-by-layer process, using the optimized toolpaths.

Lewicki, James↗
DOE OSTI2020

RODeO (Revenue Operation and Device Optimization Model) [SWR 20-67]

The Revenue, Operation, and Device Optimization (RODeO) model explores optimal system design and operation considering different levels of grid integration, equipment cost, operating limitations, financing, and credits and incentives. RODeO is a price-taker model formulated as a mixed-integer linear programming (MILP) model in the GAMS modeling platform. The objective is to maximizes the net revenue for a collection of equipment at a given site. The equipment includes generators (e.g., gas turbine, steam turbine, solar, wind, hydro, fuel cells, etc.), storage systems (batteries, pumped hydro, gas-fired compressed air energy storage, long-duration systems, hydrogen), and flexible loads (e.g., electric vehicles, electrolyzers, flexible building loads). The input data required by RODeO can be classified into three bins: 1) utility service data, which refers to retail utility rate information (meter cost, energy and demand charges), 2) electricity market data, which include energy and reserve prices, 3) other inputs, which refer to additional electrical demand, product output demand, technological assumptions, financial properties, and operational parameters.

Guerra Fernandez, Omar Jose↗
DOE OSTI2023

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

Here this paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The first stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the second stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Renewable energy integration and system operation challenge: control and optimization of millions of devices

The electric power infrastructure, originally designed and built on large-scale power plants, is evolving into a more resilient power generation and delivery system in which millions of smaller units of distributed energy generation resources units will be installed in sub-transmission and distribution networks. In order to control, manage and optimize the future grid, a hierarchical design is presented in this chapter which enables the distributed control on grid edge while inheriting the existing centralized control structure. This layered design of large-scale power system operation and control uses the following principle: reactive power control is treated as a primary control for voltage stability, and the real power control is primarily a grid-level control but can also be a supplementary control for voltage support in the case of insufficient reactive power control capacity. For the purpose of active control and operation at the distribution level, a recursive power network model is derived from nodal injection and branch power flow models. Based on the model, the proposed algorithms of hierarchical control, grid-edge inference and dynamic hosting allowance are developed and presented for multi-level controlled operation. And, a co-simulation architecture of integrated T&D system is presented to validate and demonstrate the feasibility and scalability of proposed algorithms.

Xu, Ying↗
DOE OSTI2026

Economic NMPC for a Reversible Solid Oxide Cell

Reversible solid oxide fuel cells (rSOCs) offer the flexibility to operate in tandem with the electric grid by switching between fuel cell and electrolysis modes based on real-time electricity prices. However, their complex, tightly coupled dynamic behavior poses significant challenges in determining optimal operating strategies. In this work, we present an economic nonlinear model predictive control (E-NMPC) framework to optimize the operation of rSOCs. The proposed E-NMPC is applied to a detailed rSOC flowsheet model that includes a utility scale rSOC module as well as balance-of-plant equipment necessary for thermal management. Our results demonstrate that in fuel cell mode, the E-NMPC strategy reduces hydrogen consumption compared to conventional set-point tracking NMPC, while maintaining the same level of electricity output. Also, in electrolysis mode, the E-NMPC yields a marginal improvement in hydrogen production. In addition, we explore the integration of a battery with the rSOC system to enhance flexibility in meeting electricity production and consumption targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Optimization Formulations for Storage Devices with Disjoint Operating Modes

Optimizing State of Charge for Storage Resources Storage of generated energy has existed as a contributing resource within the power sector for more than a century in the case of pumped hydro storage. Even though most regional transmission organizations/independent system operators do not currently optimize state of charge (SOC) for storage, the SOC is an essential aspect of the operating characteristics of storage. This paper investigates the impact on computational performance of optimizing storage with explicit representation of SOC. To investigate the model, the paper considers two contexts, stand-alone and large-scale. Analysis of the stand-alone context helps to explain why the combination of features in the storage model results in a difficult problem. Numerical results for large-scale test cases verify that new tighter valid inequalities can improve the computation compared with the standard model in the literature.

Business & Economics↗
DOE OSTI2021

AC Power Flow Based DLMP Calculation and Decomposition Method to Smooth Power Fluctuation of Distributed Renewable Energy Sources

As the penetration of renewable energy sources increases, the growing renewable power variability brings ramping issues to power systems. Meanwhile, the development of distributed energy resources (DERs) makes the distribution systems to provide both energy and ancillary services. To incentivise individual resources and customers to alleviate ramping issues on the demand side, a two-stage distribution locational marginal price (DLMP) calculation and decomposition method is developed to formulate the marginal power ramping price for DERs. In the first stage of the proposed method, a distribution system operator market scheduling model based on AC optimal power flow is designed to estimate the optimal operating point of the distribution system. Subsequently, the voltage and power flow constraints are linearised in stage two to calculate DLMP. Finally, based on the Lagrange function and sensitivity factors, DLMP is decomposed to the marginal costs for active/reactive power, voltage management, power loss and power variability. Case studies demonstrate that the proposed model can effectively smooth the power fluctuation and reduce the ramping flexibility requirements of distribution systems.

AC optimal power flow↗
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 OSTI2023

Bi-Level Adaptive Storage Expansion Strategy for Microgrids Using Deep Reinforcement Learning

Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. Further, the lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. Here, to demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Demand-adaptive Transit Design for Urban Transportation Hubs

In this study, we proposed a novel three-stage framework for planning the optimal demand-adaptive transit (DAT) at urban transportation hubs. Given the potential trip demand and road traffic condition, the proposed framework sequentially generates the optimal set of candidate routes, combines the outgoing routes and incoming routes at the hub, and derives the optimal fleet size and corresponding route frequency under the fixed budget. In particular, we build the route generation algorithm which maximizes passenger demand coverage with travel time deviation constraint. And a heuristic algorithm is further developed which yields near-optimal operation routes for real-time demand. The fleet optimization problem is formulated to minimize the weighted cost of energy savings, operation cost and trip revenue. We conduct comprehensive numerical experiments for planning DAT with electric buses at JFK airport in NYC using NYC taxi and for-hire vehicle trip data and GoogleMap speed data. The results show the superior performance of the proposed route generation algorithm which is able to cover citywide passenger demand with only 61 DAT routes. The results also suggest that the proposed DAT planning framework may serve over 47% of existing taxi and FHV demand by operating 18 routes using the fleet of 62 electric buses.

demand adaptive↗