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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Resilient Operating Constraints for Power Distribution Systems under Setpoint Attacks

Integration and operation of distributed generation (DG) and energy storage (ES) in power distribution systems are enabled by communication networks and embedded sensor and control devices that increase the vulnerability of the systems to cyber-threats, broadening the attack surface and making adversary actions more unpredictable. This paper proposes a methodology that uses ellipsoidal approximations to quantify the potential damage caused by successful attacks that affect, directly or indirectly, the desired operation setpoints and may drive the power distribution operation to unsafe states by violating the limits of voltage or line flows. More specifically, a new methodology is introduced to find the optimal non-symmetric operating constraints that can be imposed to each DG and ES in order to guarantee that the power distribution system is resilient to any malicious setpoints. The proposed method takes as inputs the system topology, DG and ES capabilities, and load limits to solve a convex optimization problem formulated using linear matrix inequalities (LMIs) and the power flow equations. The proposed solution is agnostic to the attacker's action or load profile and it does not require any assumption about the location or means of the attack. The numerical results on a test distribution feeder with several DG and ES illustrate how the proposed resilient operating constraints guarantee the security of the power distribution system under setpoint attacks.

Giraldo, Jairo↗
DOE OSTI2023

Co-design optimization of combined heat and power-based microgrids

With the emergent need for clean and reliable energy resources, hybrid energy systems, such as the microgrid, are widely adopted in the United States. A microgrid can consist of various distributed energy resources, for instance, combined heat and power (CHP) systems. Here, the CHP module is a distributed cogeneration technology that produces electricity and recaptures heat generated as a by-product. It is an energy-efficient technology converting heat that would otherwise be wasted to valuable thermal energy. For an optimal system configuration, this study develops a novel co-design optimization framework for CHP-based cogeneration microgrids. The framework provides the stakeholder with a method to optimize investments and attain resilient operations. The proposed co-design framework has a mixed integer programming (MIP) model that outputs decisions for both plant designs and operating controls. The microgrid considered in this study contains six components: the CHP, boiler, heat recovery unit, thermal storage system, power storage system, and photovoltaic plant. After solving the MIP model, the optimal design parameters of each component can be found to minimize the total installation cost of all components in the microgrid. Furthermore, the online costs from energy production, operation, maintenance, machine startup, and disruption-induced unsatisfied loads are minimized by solving the optimal control decisions for operations. Case studies based on designing a CHP-based microgrid with empirical data are conducted. Moreover, we consider both nominal and disruptive operational scenarios to validate the performance of the proposed co-design framework in terms of a cost-effective, resilient system.

42 ENGINEERING↗
DOE OSTI2023

Systems and methods for power management using adaptive power split ratio

Methods and systems of power management in a hybrid vehicle are disclosed. A control system of the hybrid vehicle obtains battery temperature and catalyst temperature. The control system determines (a) whether the battery temperature is within an optimal battery temperature range and (b) whether the catalyst temperature is within an optimal catalyst temperature range. The control system determines a power split ratio (PSR) based on the determination of (a) and (b). The control system controls the engine and the motor-generator based on the determined PSR.

Ramakrishnan, Kesavan↗
DOE OSTI2025

Data-Driven Mean-Corrected Recursive Estimation-Based Optimal DER Dispatch for Distribution System Voltage Control

Recent advances in smart inverters offer opportunities to mitigate adverse grid impacts caused by high penetrations of distributed photovoltaics (PV) in distribution grids, such as voltage violations. Here, this paper proposes a novel measurement-driven optimal power flow (OPF)-based distributed energy resource management system (DERMS) voltage regulation via recursive sensitivity estimation informed coordinated control of distributed PV inverters. The proposed approach leverages available grid and controllable DER measurements, eliminating reliance on system model information while being adaptive and robust to volatile operating conditions. A mean-corrected recursive ridge regression (MCRRR) algorithm is proposed for sensitivity estimation, continuously refining the sensitivity model through a closed-form solution. It effectively manages varying grid operating conditions, such as changes in power injections and topology reconfiguration, to facilitate a time-varying update of the Load Sensitivity Factors (LSF). The proposed approach is formulated as a linear programming (LP) problem and is thus scalable to larger-scale distribution systems. Its effectiveness and efficiency are demonstrated on a realistic distribution feeder with high PV penetrations in Southern California, USA.

14 SOLAR ENERGY↗
DOE OSTI2021

Big Data Analysis of Massive PMU Datasets: A Data Platform Perspective

The discovery of `event signatures' and useful insights from very large historical Phasor Measurement Unit (PMU) datasets is predicated on offline Big Data analysis approaches that rely on the generation of predictive features on a massive scale. This paper presents lessons learned from a data platform perspective towards reducing barriers to adoption of Big Data analytics against a real dataset of almost half a trillion data points drawn from over 400 PMUs distributed across the North American power grid. We demonstrate software abstractions and targeted performance optimizations that can lead to significant productivity gains for power systems researchers seeking to perform offline exploratory temporal analysis and modeling tasks, with a focus on feature generation. We describe how our optimized approach goes beyond a naive application of mainstream Big Data technologies, enabling feature generation tasks, that previously took days or even weeks, to now be completed in just a few hours.

Kumar, Vijay S.↗
DOE OSTI2021

Agent-Based Distributed Energy Resources for Supporting Intelligence at the Grid Edge

This article proposes a novel multi-agent framework that can link various forms of resources and power electronic systems into distributed energy resources (DER). The proposed multiagent architecture can also integrate DERs to a central controller for optimization and control to support the grid. To demonstrate the flexibility of this novel framework, the developed agent system is applied to a set of end-use systems. Furthermore, the agent framework is validated in hardware using controller-hardware-in-the-loop simulation platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Advanced Computing is at the Forefront of a New “Moonshot” Revolutionizing the North American Power Grid

In the 50+ years since the first humans landed on the moon, computing has grown at breakneck speed. We are faced with another challenge that is just as daunting, and just as important to overcome-modernizing the North American electric power grid-and high-performance computing (HPC) systems with specialized software will be an important element in rising to this challenge. We describe at a high level how software developed in the ExaSGD project addresses this "moonshot" goal by utilizing exascale computing and a novel high performance solver software stack to support the mission of decarbonizing power grid operations in an environment of uncertain weather and climate. To reach the exascale benchmark the team has made a number of first-of-their-kind innovations, including novel method for stochastic optimization, fine grained parallel methods for modeling power systems, and GPU resident sparse numerical linear solvers.

17 WIND ENERGY↗
NASA NTRS1991

Sunmaster: An SEP cargo vehicle for Mars missions

Options are examined for an unmanned solar powered electric propulsion cargo vehicle for Mars missions. The 6 prime areas of study include: trajectory, propulsion system, power system, supporting structure, control system, and launch consideration. Optimization of the low thrust trajectory resulted in a total round trip mission time just under 4 years. The argon propelled electrostatic ion thruster system consists of seventeen 5 N engines and uses a specific impulse of 10,300 secs. At Earth, the system uses 13 engines to produce 60 N of thrust; at Mars, five engines are used, producing 25 N thrust. The thrust of the craft is varied between 60 N at Earth and 24 N at Mars due to reduced solar power available. Solar power is collected by a Fresnel lens concentrator system using a multistacked cell. This system provides 3.5 MW to the propulsion system after losses. Control and positioning to the craft are provided by a system of three double gimballed control moment gyros. Four shuttle 'C' launches will be used to transport the unassembled vehicle in modular units to low Earth orbit where it will be assembled using the Mobile Transporter of the Space Station Freedom.

Chiles, Aleasa↗
DOE OSTI2020

A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems

The aim in this work is to develop an optimization framework to address cycling of baseload energy systems due to the penetration of renewables into the grid. The developed strategy corresponds to a multi-objective and dynamic real-time optimization (MOO-DRTO) framework applied to a postcombustion MEA-based CO 2 capture process from a baseload coal-fired power plant under cycling conditions. A Tchebycheff-based method is used for the multi-objective optimization (MOO) component. Also, a hybrid approach consisting of Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) is implemented for the first time in process systems engineering to solve the dynamic real-time optimization (DRTO) component. The objectives considered in the MOO-DRTO framework are economic and environmental. The proposed MOO-DRTO strategy is successfully implemented and 24-h optimal output trajectories for the carbon capture system under cycling are generated. Also, the optimal compromise is chosen from the Pareto front according to a set of selected weights for the objectives with minimal interaction between the framework and decision maker. The results indicated that the developed framework has potential to be extended to plant-wide optimization applications under cycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Models and Strategies for Optimal Demand Side Management in the Chemical Industries

Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Convex Optimization with Smart Grid Examples

In this talk, we give an overview of the field of convex optimization and work through four canonical problems that relate to electrical power systems and smart grids. The purpose of these examples is to demonstrate the breadth of applications of convex optimization in energy research and to show that toy versions of these problems can be solved in just a few lines of code, indicating the scale and complexity of problems that can be tackled with a more detailed treatment. We emphasize the cvxpy modeling language as a foundational technology that enables rapid development and prototyping of convex optimization problems, allowing researchers to focus on model development rather than get caught in the weeds of numerical and code implementation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world.

15 GEOTHERMAL ENERGY↗
NASA NTRS1974

Dynamic conversion of solar generated heat to electricity

The effort undertaken during this program led to the selection of the water-superheated steam (850 psig/900 F) crescent central receiver as the preferred concept from among 11 candidate systems across the technological spectrum of the dynamic conversion of solar generated heat to electricity. The solar power plant designs were investigated in the range of plant capacities from 100 to 1000 Mw(e). The investigations considered the impacts of plant size, collector design, feed-water temperature ratio, heat rejection equipment, ground cover, and location on solar power technical and economic feasibility. For the distributed receiver systems, the optimization studies showed that plant capacities less than 100 Mw(e) may be best. Although the size of central receiver concepts was not parametrically investigated, all indications are that the optimal plant capacity for central receiver systems will be in the range from 50 to 200 Mw(e). Solar thermal power plant site selection criteria and methodology were also established and used to evaluate potentially suitable sites. The result of this effort was to identify a site south of Inyokern, California, as typically suitable for a solar thermal power plant. The criteria used in the selection process included insolation and climatological characteristics, topography, and seismic history as well as water availability.

Powell, J. C.↗
DOE OSTI2020

Convex Relaxation of Grid-Connected Energy Storage System Models With Complementarity Constraints in DC OPF

Including complementarity constraints in energy storage system (ESS) models in optimization problems ensure an optimal solution will not produce a physically unrealizable control strategy where there is simultaneous charging and discharging. However, the current approaches to impose complementarity constraints require the use of non-convex optimization methods. Here, we propose a convex relaxation for a common ESS model that has terms for both charging and discharging based on a penalty reformulation for use in a model predictive control (MPC) based optimal power flow (DC OPF) problem. In this approach, the complementarity constraints are omitted and a penalty term is added to the optimization objective function. For the DC OPF problem, we provide analysis for the conditions under which the convex relaxation of the complementarity constraint ensures that a solution with simultaneous ESS charging and discharging operation is suboptimal. Simulation results demonstrating ESS behavior with and without the penalty reformulation are provided for an MPC-based DC OPF problem on multiple IEEE test systems.

25 ENERGY STORAGE↗
NASA NTRS2014

The Use of Software Agents for Autonomous Control of a DC Space Power System

In order to enable manned deep-space missions, the spacecraft must be controlled autonomously using on-board algorithms. A control architecture is proposed to enable this autonomous operation for an spacecraft electric power system and then implemented using a highly distributed network of software agents. These agents collaborate and compete with each other in order to implement each of the control functions. A subset of this control architecture is tested against a steadystate power system simulation and found to be able to solve a constrained optimization problem with competing objectives using only local information.

autonomous control↗
DOE OSTI2025

Optimal Control for Fast Frequency Response and Black-Start using Embedded Storages with Grid-Forming Control

The grid-forming inverter is regarded as the solution for integrating high levels of renewable resources into future power systems. Ensuring the stable operation of grids necessitates that grid-forming inverters offer fast frequency response. This report introduces an optimal control method that coordinates the embedded storage within the grid-forming control model with conventional synchronous generators. The optimized active power reference for the embedded storage is generated using receding horizon optimization control, aiming to keep the center of inertia frequency within acceptable limits. The effectiveness of the proposed control is verified through testing in the IEEE 39-bus system. In addition, with grid-forming capability, we will also investigate the application of using mobile embedded storages as black-start units to provide cranking power to energize non-blackstart generators in a black-start process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2014

Controllable Multi-Functional Heat Exchanger for Space Applications

Team Spacering will design SpaceRing, a cooling and waste heat recycling system that addresses NASA’s interest for a cooling and power-generating unit in its prototype deep space habitat[1]. The merits of this system are as follows: - Controllable cooling is critical in maintaining a habitable environment and in the functionality of life support systems, powering electronics and other devices in a spacecraft. - Power is expensive to produce in a space environment. Converting wasted thermal energy into electricity will save energy and money for the space station. Space habitation is a highly complex endeavor. Consequently, the SpaceRing must adhere to additional constraints, which include: - Lack of natural fluid or gas convection due to micro-gravity environment - Limited range for heat source and cold sink temperature - Subject to high fluctuations of incoming thermal energy - Minimal weight due to the high cost of transporting items into space After consulting with NASA and experts in thermal studies, several design objectives were identified and are listed below in order of importance: - High cooling capacity - Reliable safety control and alert system - Maximization of energy generation from wasted heat - Optimization of cooling capability and power generation - Long operational life and minimization of maintenance In space, conventional cooling systems utilize passive heat pipe systems for small-scale heat rejection and pumped pipes with heat exchangers for large-scale heat rejection. Thermal power generation in space is mainly achieved by using thermoelectric material that produces electric energy from temperature differentials. We propose a design that combines cooling and thermoelectric power generation. Using a hot pipe connected to a heat source, a cold pipe connected to a radiator, a heat exchanger in contact with both pipes, and a pump in each pipe to move the fluid, we make an active cooling system. The thermoelectric material is inserted in the heat exchanger between the hot and cold pipes to extract energy from the maximum temperature difference coming from the two pipes. It also provides controllable cooling capacity through the use of the thermoelectric material as cooling modules. We have tested our system under target heat source and sink conditions and measured the temperatures and flow rates. Our preliminary results are promising, demonstrating the output of 25 Watts of power on average. We are in the process of running control tests to compare the heat transfer performance without the thermoelectric generators, which is vital in determining whether or not the generators introduce an unacceptable amount of thermal resistance. While some control tests have been run already, the results have been noisy. We have modified our testing environment and data acquisition methods to allow for simple comparison between the control test results and thermoelectric performance results. Lastly, we have modified the heat exchanger design slightly to include acrylic baffles. This will allow the heat to flow through the heat exchanger more efficiently. We have presented our preliminary results to NASA and will deliver the final product by the end of May 2014.

Lee Xiong↗