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DOE OSTI2020

Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

model predictive control↗

NASA NTRS2004

Lunar Navigation with Libration Point Orbiters and GPS

NASA is currently studying a Vision for Space Exploration based on spiral development of robotic and piloted missions to the moon and Mars, but research into how to perform such missions has continued ever since the first era of lunar exploration. One area of study that a number of researchers have pursued is libration point navigation and communication relay concepts. These concepts would appear to support many of NASA's current requirements for navigation and communications coverage for human and robotic spacecraft operating in lunar space and beyond. In trading libration point concepts against other options, designers must consider issues such as the number of spacecraft, required to provide coverage, insertion and stationkeeping costs, power and data rate requirements, frequency allocations, and many others. The libration points, along with a typical cis-lunar trajectory, are equilibrium locations for an infinitesimal mass in the rotating coordinate system that follows the motion of two massive bodies in circular orbits with respect to their common barycenter. There are three co-linear points along the line connecting the massive bodies: between the bodies, beyond the secondary body, and beyond the primary body. The relative distances of these points along the line connecting the bodies depend on the mass ratios. There are also two points that form equilateral triangles with the massive bodies. Ideally, motion in the neighborhood of the co-linear points is unstable, while motion near the equilibrium points is stable. However, in the real world, the motions are highly perturbed so that a satellite will require stationkeeping maneuvers.

Carpenter, J. Russell↗

DOE OSTI2020

Probabilistic Zonal Reserve Requirements for Improved Energy Deliverability with Wind Power

In power systems with high penetration of renewable energy resources, uncertainty and variability of these stochastic resources introduce additional challenges for the operation of power systems. To improve the power system's reliability in the face of uncertainty, reserves are required as additional generation capacity to rebalance the power system following random disturbances. However, reserve deliverability is not guaranteed, because it may encounter potential transmission line congestion. Zonal reserve requirements can address this issue, but operators lack efficient ways to allocate reserves to zones while accounting for wind power forecast uncertainty. Herein we propose a methodology for probabilistic zonal reserve requirements to address wind power forecast uncertainties. This method estimates the probability distribution of line flows based on the system generation margin and injection shift factor. This estimate is then used to construct pre-defined and post-zonal reserve requirements. Case studies demonstrate that the proposed method efficiently schedules energy and reserves to balance energy and manage deliverability with wind power forecast uncertainty.We also discuss operational implications of the proposed method.

17 WIND ENERGY↗

DOE OSTI2020

Machine Learning-Based Prediction of Distribution Network Voltage and Sensors Allocation: Preprint

Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗

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 OSTI2020

Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

61 RADIATION PROTECTION AND DOSIMETRY↗

NASA NTRS2004

Dust Accumulation and Solar Panel Array Performance on the Mars Exploration Rover (MER) Project

One of the most fundamental design considerations for any space vehicle is its power supply system. Many options exist, including batteries, fuel cells, nuclear reactors, radioisotopic thermal generators (RTGs), and solar panel arrays. Solar arrays have many advantages over other types of power generation. They are lightweight and relatively inexpensive, allowing more mass and funding to be allocated for other important devices, such as scientific instruments. For Mars applications, solar power is an excellent option, especially for long missions. One might think that dust storms would be a problem; however, while dust blocks some solar energy, it also scatters it, making it diffuse rather than beamed. Solar cells are still able to capture this diffuse energy and convert it into substantial electrical power. For these reasons, solar power was chosen to be used on the 1997 Mars Pathfinder mission. The success of this mission set a precedent, as NASA engineers have selected solar power as the energy system of choice for all future Mars missions, including the Mars Exploration Rover (MER) Project. Solar sells have their drawbacks, however. They are difficult to manufacture and are relatively fragile. In addition, solar cells are highly sensitive to different parts of the solar spectrum, and finding the correct balance is crucial to the success of space missions. Another drawback is that the power generated is not a constant with respect to time, but rather changes with the relative angle to the sun. On Mars, dust accumulation also becomes a factor. Over time, dust settles out of the atmosphere and onto solar panels. This dust blocks and shifts the frequency of the incoming light, degrading solar cell performance. My goal is to analyze solar panel telemetry data from the two MERs (Spirit and Opportunity) in an effort to accurately model the effect of dust accumulation on solar panels. This is no easy process due to the large number of factors involved. Changing solar flux (the amount of solar energy reaching the planet), solar spectrum, solar angle, rover tilt, and optical depth (the opacity of the atmosphere due to dust) were the most significant. Microsoft Excel and Visual Basic are used for data analysis. The results of this work will be used to improve the dust accumulation and atmosphere effects model that was first created after the Mars Pathfinder mission. This model will be utilized and applied when considering the design of solar panel array systems on future Mars projects. Based on this data, and depending upon the tenure and application of the mission, designers may also elect to employ special tools to abate dust accumulation, or decide that the expected level of accumulation is acceptable.

Turgay, Eren H.↗

DOE OSTI2023

Distribution Network Capacity Market Design: Marginal Distribution Capacity Pricing Mechanism for Efficient Investment and Cost Allocation

As electricity markets begin to shift from reliance on large, centralized power plants and towards distributed energy resources (DERs), there is a growing acknowledgement that more efficient planning, operations, and oversight is needed in the distribution system. This paper addresses one step in that direction by proposing an auction mechanism that uses a detailed distribution system planning model that allocates permits to end-used customers who request capacity to install new devices at their location and network upgrade contracts to utilities or 3rd-party companies who offer to upgrade system components. The resulting plan maximizes market surplus, that is, maximizes the total benefit to consumers minus the cost of network upgrades. We apply a marginal pricing scheme to the auction’s results such that the cost of each permits or contracts is differentiated by time and location, based on the Lagrangian multipliers of binding network constraints. These prices are shown to be no greater than the bid price of any awarded contract and no less than the offered cost of any awarded upgrade contract. Furthermore, the nonlinearity of power flows in the planning model result in an additional surplus that would be collected by the entity that hosts the auction, which could then be refunded to market participants or used to cover overhead costs of running the market. We provide four example auction results in a simple three-node distribution feeder to demonstrate the properties of the design. Results suggest that larger or more realistic case studies could be a promising next step.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2001

Integrated Neural Flight and Propulsion Control System

This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

Kaneshige, John↗

DOE OSTI2023

Nonlinear control of safety factor gradient in tokamaks using spatially variable electron cyclotron current drives

Active control of plasma properties may be necessary to achieve stable operation of next-generation tokamaks over large time scales. Such control algorithms can regulate the plasma properties to avoid the onset of magnetohydrodynamic (MHD) instabilities. For instance, the global and local properties of the safety factor profile are linked to the onset of neoclassical tearing modes (NTMs). This work proposes a model-based control approach for deterring/delaying NTMs through active modulation of one of the safety factor properties - the gradient of the safety factor profile at a particular rational safety factor surface. In particular, a novel control-oriented model for the local safety factor gradient is developed. The nonlinear control model is governed by a nonautonomous ordinary differential equation that accounts for a given rational safety factor surface’s spatial variation over time. Further, to improve the controllability of the spatially evolving parameter, the control model treats ECH&CD positions, along with noninductive powers, as controllable variables. A nonlinear control algorithm based on feedback linearization with optimization is synthesized to achieve the objective of regulating the safety factor gradient around a given target. The proposed algorithm allocates optimal ECH&CD positions, in addition to auxiliary powers, at each time instant as the rational safety factor surface drifts to locations with a low control authority. Stability guarantees of the proposed control law are also discussed in this work. The proposed algorithm is tested for a DIII-D tokamak scenario in nonlinear simulations carried out using the Control Oriented Transport SIMulator (COTSIM). Both fixed and moving ECH&CD cases are studied, and their outcomes are compared. Simulation results demonstrate that enthusiastic regulation of the safety factor gradient can be achieved during the ramp-up and flat-top phases of tokamak operation in both fixed and moving ECH&CD cases. However, real-time updates of ECH&CD positions can prevent the saturation of auxiliary powers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Planning for Resilient Power Distribution Systems using Risk-Based Quantification and Q-Learning

Grid hardening is one of the most effective approaches that reduce the component failures and restoration efforts thus increasing the resilience of the power systems against extreme events. However, hardening and upgrading the entire system is prohibitively expensive and hence the optimal design of a distribution network is challenging. This paper adopted a reinforcement learning algorithm to identify the optimal hardening strategy to enhance the resilience of power distribution systems. Adopting the Q-learning algorithm as the reinforcement learning technique, we found the sequential optimal action for hardening measures to enhance the grid's resilience for the given budget. To identify the optimal strategy through Q-learning, Conditional Value at Risk (CVaR) is used as a rewarding metric. A study on the IEEE 123-bus test feeder validate the effectiveness of the proposed model and show how to effectively allocate budget limited resources to plan a resilient power distribution network.

Paul, Shuva↗

NASA NTRS1982

Reliability and performance experience with flat-plate photovoltaic modules

Statistical models developed to define the most likely sources of photovoltaic (PV) array failures and the optimum method of allowing for the defects in order to achieve a 20 yr lifetime with acceptable performance degradation are summarized. Significant parameters were the cost of energy, annual power output, initial cost, replacement cost, rate of module replacement, the discount rate, and the plant lifetime. Acceptable degradation allocations were calculated to be 0.0001 cell failures/yr, 0.005 module failures/yr, 0.05 power loss/yr, a 0.01 rate of power loss/yr, and a 25 yr module wear-out length. Circuit redundancy techniques were determined to offset cell failures using fault tolerant designs such as series/parallel and bypass diode arrangements. Screening processes have been devised to eliminate cells that will crack in operation, and multiple electrical contacts at each cell compensate for the cells which escape the screening test and then crack when installed. The 20 yr array lifetime is expected to be achieved in the near-term.

Ross, R. G., Jr.↗

DOE OSTI2020

Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

Increasing penetration levels of fast-varying energy resources might negatively affect power system operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation of voltage violation scenarios. This paper analyzes various approaches to voltage prediction in a distribution system, and it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed in which initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed to perform sensor allocation so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Demonstration and Evaluation of the Human-Technology Integration Function Allocation Methodology

There is an imminent need for the existing nuclear power plants to reduce their operating and maintenance (O&M) costs to remain economically viable. Digital technology, including automation, provides a significant opportunity for the existing nuclear power plant fleet to transform the way in which work is accomplished, reducing O&M costs, and allowing the fleet to remain economically competitive. One notable opportunity to significantly reduce O&M costs pertains to modifications to the plant equipment and main control room (MCR). Existing instrumentation and control (I&C) technologies in the MCR are highly analog, costly to operate and maintain, and demand a high cognitive and physical workload from plant staff (i.e., operators). Digitalizing the MCR has a range of broad economic benefits, including improved plant performance and reduced manual work. Further, digital I&C systems can fundamentally change the way in which plant staff operate the plant; this is the concept of operation. Human-technology integration is important to ensure that impacts to the concept of operation are done in a way that account for capabilities of people and technology. Human-technology integration employs human factors engineering (HFE) methods and principles to maximize the benefits of digital technology, reducing human error, improving overall decision-making and usability. The U.S. Department of Energy Light Water Reactor Sustainability Program is applying human-technology integration research to ensure digital technologies are safe, reliable, and efficient. This paper documents the demonstration of the human-technology guidance developed by the Light Water Reactor Sustainability Program from a first-of-a-kind digital I&C upgrade, specifically addressing function analysis and allocation for a new digital I&C system that included changes in automation levels. The program’s specific approach is included in this work, following lessons learned. This document serves as a resource for industry to follow in applying human-technology integration and HFE to digital modifications, specific to function analysis and allocation. The lessons learned should be considered in the planning and execution of HFE activities that support such digital modifications.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2014

Low-Cost, Class D Testing of Spacecraft Photovoltaic Systems Can Reduce Risk

The end-to-end verification of a spacecraft photovoltaic power generation system requires light! Specifically, the standard practice for doing so is the Large Area Pulsed Solar Simulation (LAPSS). A LAPSS test can characterize a photovoltaic system's efficiency via its response to rapidly applied impulses of simulated sunlight. However, a Class D program on a constrained budget and schedule may not have the resources to ship an entire satellite for a LAPSS test alone. Such was the case with the Lunar Atmospheric and Dust Environment Explorer (LADEE) program, which was also averse to the risk of hardware damage during shipment. When the Electrical Power System (EPS) team was denied a spacecraft-level LAPSS test, the lack of an end-to-end power generation test elevated to a project-level technical risk. The team pulled together very limited resources to not only eliminate the risk, but build a process to monitor the health of the system through mission operations. We discuss a process for performing a low-cost, end-to-end test of the LADEE photovoltaic system. The approach combines system-level functional test, panel-level performance results, and periodic inspection (and repair) up until launch. Following launch, mission operations tools are utilized to assess system performance based on a scant amount of data. The process starts in manufacturing at the subcontractor. The panel manufacturer provides functional test and LAPSS data on each individual panel. We apply an initial assumption that the per-panel performance is sufficient to meet the power generation requirements. The manufacturer's data is also carried as the performance allocation for each panel during EPS system modeling and initial mission operations. During integration and test, a high-power, professional theater lamp system provides simulated sunlight to each panel on the spacecraft, thereby permitting a true end-to-end system test. A passing test results in a step response to nearly full-rated current at the appropriate solar array switch in the power system. A metal-halide bulb, infrared imagers, and onboard spacecraft measurements are utilized to minimize risk of thermal damage during test. Data is provided to support test results for both passing and marginal panels. Prior to encapsulation in the launch vehicle, each panel is inspected for damage by the panel manufacturer. Cracked cells or other damage is amended on-site. Because the photovoltaic test system is inexpensive and portable, each repaired panel can be re-verified immediately. Post-launch, the photovoltaic system is again characterized for per-panel deviations from the manufacturer's performance test. This proved especially tricky as the LADEE spacecraft performs only one current measurement on the entire array. The algorithm for Matlab tools to assess panel performance based on spacecraft attitude is discussed. While not as precise and comprehensive as LAPSS, the LADEE approach leverages minimal resources into an ongoing assessment program that can be applied through numerous stages of the mission. The project takes a true Class D approach in assessing the technical value of a spacecraft level performance test versus the programmatic risk of shipping the spacecraft to another facility. The resources required are a fraction of that for a LAPSS test, and is easy to repeat. Further, the test equipment can be handed down to future projects without building an on-site facility.

photovoltaic↗

NASA NTRS2004

Optimal Sensor Allocation for Fault Detection and Isolation

Automatic fault diagnostic schemes rely on various types of sensors (e.g., temperature, pressure, vibration, etc) to measure the system parameters. Efficacy of a diagnostic scheme is largely dependent on the amount and quality of information available from these sensors. The reliability of sensors, as well as the weight, volume, power, and cost constraints, often makes it impractical to monitor a large number of system parameters. An optimized sensor allocation that maximizes the fault diagnosibility, subject to specified weight, volume, power, and cost constraints is required. Use of optimal sensor allocation strategies during the design phase can ensure better diagnostics at a reduced cost for a system incorporating a high degree of built-in testing. In this paper, we propose an approach that employs multiple fault diagnosis (MFD) and optimization techniques for optimal sensor placement for fault detection and isolation (FDI) in complex systems. Keywords: sensor allocation, multiple fault diagnosis, Lagrangian relaxation, approximate belief revision, multidimensional knapsack problem.

Azam, Mohammad↗

NASA NTRS1973

Study of design and control of remote manipulators. Part 2: Vibration considerations in manipulator design

An investigation is reported involving a mathematical procedure using 4 x 4 transformation matrices for analyzing the vibrations of flexible manipulators. Previous studies with the procedure are summarized and the method is extended to include flexible joints as well as links, and to account for the effects of various power transmission schemes. A systematic study of the allocation of structural material and the placement of components such as motors and gearboxes was undertaken using the analytical tools developed. As one step in this direction the variables which relate the vibration parameters of the arm to the task and environment of the arm were isolated and nondimensionalized. The 4 x 4 transformation matrices were also used to develop analytical expressions for the terms of the complete 6 x 6 compliance matrix for the case of two flexible links joined by a rotating joint, flexible about its axis of rotation.

Book, W. J.↗

NASA NTRS1979

The critical satellite technical issues of future pervasive broadband low-cost communication networks

The critical technical issues of signal waveform design, projected spacecraft technology, satellite launch options, and satellite cost are discussed for future pervasive broadband communication networks. With DPCM video signal encoding, 32 Mb/s user-to-user data rate per channel, 10% overhead, two orthogonal polarizations, and crosstalk loss limited to 1 dB, TFM permits about 75 channels/GHz of frequency allocation. The BOM (beginning of mission) weight and power of a baseline 400-channel multibeam satellite is about 1800 kg and 5000 W. Each 35 Mb/s channel can support 1 to 10 video channels. The weight and power estimates assume hardened digital logic, composite materials for a multibeam antenna structure, high-efficiency solar cells, batteries, and amplifiers. Based on a cost model for large communication satellites, the total space segment cost of two active satellites and one spare would be about $485 M.

Harvey, R. L.↗