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At least 145 records · Page 8

Survey of baffle plate samples, specimen machining, and hydrogen measurements

This report outlines preliminary results on the survey and characterization of irradiated baffle plate, baffle former, and flux thimble tube specimens as part of the ongoing investigation into irradiation-induced embrittlement in austenitic stainless steels at ambient (room temperature) conditions. The specimens originated from commercial pressurized water reactor (PWR) components, covering displacement damage doses ranging from approximately 0.065 to 75 dpa. Initial scanning electron microscopy (SEM) surveys revealed that specimen surfaces exhibited fine machining marks and in-service-formed oxide layers on the side surfaces of the analyzed specimens. The oxide layers revealed specific features resembling localized "pitting-like" corrosion. An additional set of specimens representing in-service oxidation is being preserved for future microstructure analysis work. As believed, this will provide additional insights into long-term material degradation in PRWs. Specimen machining challenges emerged due to a complex failure of the electric discharge machining (EDM) system located in hot area. Despite partial restoration, issues persist with the EDM’s secondary power supply, necessitating the exploration of alternative EDM or computer numerical control (CNC) machining approaches to facilitate tensile specimen preparation. Currently, low speed saw cutting is in progress to prepare specimens for hydrogen measurements and general microstructure analysis. The near-term goals include completing tensile specimen machining for advanced mechanical testing and characterizing fracture mechanisms, and stress-corrosion cracking testing, ultimately aiming to identify and mitigate the ambient-condition intergranular cracking through targeted post-irradiation annealing strategies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Guest Editorial: Planning and operation of resilient distribution system for integrated multi-energy

Resilience is the ability of power systems to prepare for and adapt to low-probability, high-impact incidents and withstand and recover rapidly from disruptions. With the ageing of electricity distribution infrastructure and increasing threats of weather-related incidents and natural disasters, the need to effectively enhance the resilience of the electricity distribution system has become urgent and has attracted worldwide attention. Although there are an increasing number of publications related to enhancing resilience strategies, resilience is an emerging concept in power systems. Existing practices are mostly focused on deploying distributed energy resources (DERs) and microgrids, hardening the existing infrastructures and building redundant capacities. However, from a broader perspective, resilience enhancement of power distribution system is a systematic engineering, involving long-term system planning and upgrading (e.g., deployment of smart grid technologies and intelligent switches), short-term proactive scheduling, real-time robust and resilient control of DERs, and post-event restoration and recovery strategies. Based on this point, this Special Issue in IET Energy System Integration focuses on soliciting the most recent and original technologies, scheduling and control strategies for improving the resilience of power distribution system. Eight papers are presented in this Special Issue, covering various aspects related to resilience enhancement of power distribution system, including fault-tolerant frequency measurement, robust scheduling of integrated electricity and district heating systems, robust control of DERs, efficient methods for safety verification as well as novel graph theory-based approach to restore the distribution systems after multiple simultaneous faults. A brief introduction of these 8 papers is provided below.

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Safe Exploration Reinforcement Learning for Load Restoration using Invalid Action Masking

This paper addresses the load restoration problem after a power outage event. Our primary proposed methodology uses a multi-agent reinforcement learning method to make the optimal sequential decisions on picking up critical loads. Typically, a negative reward is provided to discourage the agents from selecting decisions that violate physical constraints during the restoration process. However, the main disadvantage of this approach is its difficulty in applying it to large-scale systems due to the curse of dimensionality. This paper introduces the invalid action masking technique to overcome this limitation. The features of this technique include zero physical constraint violations, reduced training time, and stabilization of the explo- ration process. Simulation results are performed in IEEE 13-node and IEEE 123-node systems showing the better performance of the proposed algorithm in comparison to the conventional approaches both in terms of restored power and learning curve.

reinforcement learning, blackstart, artificial int↗

Advanced Stirling Convertor Control Unit Testing at NASA Glenn Research Center in the Radioisotope Power Systems System Integration Laboratory

Future NASA missions could include long-duration flyby, orbital, lander, or rover applications where generating power from sunlight may be limited. Radioisotope power systems (RPSs) provide a dependable power source for missions where inadequate sunlight or operational requirements make other power systems impractical. Over the past 16 years, NASA Glenn Research Center has been supporting the development of RPSs. The advanced Stirling radioisotope generator (ASRG) utilized a pair of advanced Stirling convertors (ASCs). Although flight development of the ASRG has been canceled, much of the technology and hardware continued development and testing to guide future activities. Specifically, a controller for the convertor(s) is an integral part of a Stirling-based RPS. For the ASRG design, the controller maintains stable operation of the convertors, regulates the alternating current produced by the linear alternator of the convertor, provides a specified direct-current output voltage for the spacecraft, and synchronizes the piston motion of the two convertors to minimize vibration as well as manage and maintain operation with a stable piston amplitude and hot-end temperature. It not only provides power to the spacecraft but also must regulate convertor operation to avoid damage to internal components and maintain safe thermal conditions after fueling. Lockheed Martin Coherent Technologies, Inc., has designed, developed, and tested an ASC control unit engineering development unit (ACU EDU) to support this effort. GRC used the ACU EDU as part of its nonnuclear representation of a RPS that also consists of a Dual advanced Stirling convertor simulator (DASCS), and associated support equipment to perform a test in the Radioisotope Power Systems System Integration Laboratory (RSIL). The RSIL was designed and built with flexibility to evaluate hardware utilizing RPS technology. The RSIL provides insight into the electrical interactions between as many as three radioisotope power generators, associated control strategies, and typical electric system loads. The first phase of testing included a DASCS that was developed by Johns Hopkins University Applied Physics Laboratory and simulates the operation and electrical behavior of a pair of ASCs in real time via a combination of hardware and software. Testing included the following spacecraft electrical energy storage configurations: capacitor, battery, and supercapacitor. Testing of the DASCS and ACU in each energy storage configuration included simulation of a typical mission profile and transient voltage and current data during load turnon and turnoff. Testing for these devices also included the initiation of several system faults such as short circuits, electrical bus overvoltage, undervoltage, and a "dead bus" recovery to restore normal power operations. The goal of this testing was to verify operation of the ACU(s) when connected to a spacecraft electrical bus. The results of these tests are presented here.

General↗

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained offline using a historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery. Case studies demonstrated that the proposed method outperforms other policies with static operating reserves.

distribution system↗

Ghost suppression in image restoration filtering

An optimum image restoration filter is described in which provision is made to constrain the spatial extent of the restoration function, the noise level of the filter output and the rate of falloff of the composite system point-spread away from the origin. Experimental results show that sidelobes on the composite system point-spread function produce ghosts in the restored image near discontinuities in intensity level. By redetermining the filter using a penalty function that is zero over the main lobe of the composite point-spread function of the optimum filter and nonzero where the point-spread function departs from a smoothly decaying function in the sidelobe region, a great reduction in sidelobe level is obtained. Almost no loss in resolving power of the composite system results from this procedure. By iteratively carrying out the same procedure even further reductions in sidelobe level are obtained. Examples of original and iterated restoration functions are shown along with their effects on a test image.

Riemer, T. E.↗

Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience

In this work, a post-disaster distribution system repair and restoration (DSRR) strategy is proposed to improve distribution system resilience. The DSRR strategy is formulated as a two-stage optimization. The first stage is a comprehensive co-optimization of repair crew scheduling, dynamic network reconfiguration, and distributed energy resource (DER) dispatch based on the forecast load profile. The goal is to minimize the accumulative operating cost caused by the load reduction payment as well as DER operating cost. In particular, since the number of available repair crews is usually smaller than the number of faulted lines after a disaster event, the DSRR strategy determines the optimal scheduling for repairing faulted lines. The second stage is a re-dispatch of the DER power output and load shedding based on the real-time load demand of each bus. The proposed algorithm is validated by case studies of the IEEE 33-bus and 123-bus test systems. We consider those scenarios in which faults occur in multiple heavy-loaded feeders. The simulation results demonstrate that the DSRR strategy effectively coordinate the repair scheduling, network reconfiguration and load shedding to minimize the operating cost.

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Principles for Equitable Transmission Planning

Energy justice is gaining traction in the planning processes that govern the generation and supply of electric power in the U.S., with a growing number of federal and state authorities applying it as a framework to ensure that the benefits of a clean energy future extend to all levels of society. However, the principles of energy justice have not yet been integrated into electric transmission planning processes, which are designed to meet established reliability criteria and have not historically considered externalities like social inequities. This paper presents a review of transmission planning requirements in the U.S. and existing literature on energy justice, then synthesizes them to propose a framework for incorporating energy justice principles into transmission planning processes to yield more equitable outcomes. By applying procedural and recognition justice principles to create more extensive, inclusive, and diverse public participation in the planning process, planners can gain insight into—and begin to correct—the inequities created by previous planning decisions. Equipped with this understanding and informed by affected communities, planners can improve the distributional outcomes of the planning process and identify opportunities for energy system investments that will restore communities and place them on more equitable footing.

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Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

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Switching Device-Cognizant Sequential Distribution System Restoration

This paper presents an optimization framework for sequential reconfiguration using an assortment of switching devices and repair process in distribution system restoration. Compared to existing studies, this paper considers types, capabilities and operational limits of different switching devices, making it applicable in practice. We develop a novel multi-phase method to find the optimal sequential operation of various switching devices and repair faulted areas. We consider circuit breakers, reclosers, sectionalizers, load breaker switches, and fuses. The switching operation problem is decomposed into two mixed-integer linear programming (MILP) subproblems. The first subproblem determines the optimal network topology and estimates the number of steps to reach that topology, while the second subproblem generates a sequence of switching operations to coordinate the switches. For repairing the faults, we design an MILP model that dispatches repair crews to clear faults and replace melted fuses. After clearing a fault, we update the topology of the network by generating a new sequence of switching operations, and the process continues until all faults are cleared. To improve the computational efficiency, a network reduction algorithm is developed to group line sections, such that only switchable sections are present in the reduced network. The proposed method is validated on the IEEE 123-bus and 8500-bus systems.

distribution system↗

Symmetry-projected spin-AGP methods applied to spin systems

Symmetry-projected wave function methods capture static correlation by breaking and restoring the symmetries of a system. In this article, we present the symmetry-projected spin antisymmetrized geminal power (spin-AGP) state projected onto space group symmetry as well as complex conjugation, spin-flip, and time-reversal symmetries. The method is benchmarked on the 1D XXZ model and the 2D J 1 − J 2 model with square and triangular lattices. Our results indicate that symmetry projection methods provide a powerful tool for frustrated spin systems.

Antisymmetrized geminal power↗

Adaptive digital beamforming for a CDMA mobile communications payload

In recent years, Spread-Spectrum Code Division Multiple Access (CDMA) has become a very popular access scheme for mobile communications due to a variety of reasons: excellent performance in multipath environments, high scope for frequency reuse, graceful degradation near saturation, etc. In this way, a CDMA system can support simultaneous digital communication among a large community of relatively uncoordinated users sharing a given frequency band. Nevertheless, there are also important problems associated with the use of CDMA. First, in a conventional CDMA scheme, the signature sequences of asynchronous users are not orthogonal and, as the number of active users increases, the self-noise generated by the mutual interference between users considerably degrades the performance, particularly in the return link. Furthermore, when there is a large disparity in received powers - due to differences in slant range or atmospheric attenuation - the non-zero cross-correlation between the signals gives rise to the so-called near-far problem. This leads to an inefficient utilization of the satellite resources and, consequently, to a drastic reduction in capacity. Several techniques were proposed to overcome this problem, such as Synchronized CDMA - in which the signature sequences of the different users are quasi-orthogonal - and power control. At the expense of increased network complexity and user coordination, these techniques enable the system capacity to be restored by equitably sharing the satellite resources among the users. An alternative solution is presented based upon the use of time-reference adaptive digital beamforming on board the satellite. This technique enables a high number of independently steered beams to be generated from a single phased array antenna, which automatically track the desired user signal and null the unwanted interference source. In order to use a time-reference adaptive antenna in a communications system, the main challenge is to obtain a reference signal highly correlated with the desired user signal and uncorrelated with the interferences. CDMA lends itself very easily to the generation of such a reference signal, thanks to the a priori knowledge of the user's signature sequence. First, the integration of an adaptive antenna in an asynchronous CDMA system is analyzed. The adaptive antenna system can provide increased interference rejection - much higher than that afforded by the code alone - and, since CDMA is mainly interference limited, any reduction in interference converts directly and linearly into an increase in capacity. Analyses and computer simulations are presented that show how an asynchronous CDMA system incorporating adaptive beamforming can provide at least as much capacity as a synchronous system. More importantly, the proposed concept allows the near-far effect to be mitigated without requiring a tight coordination of the users in terms of transmitted power control or network synchronization. The system is extremely robust to the near-far effect because the signals reaching the satellite from directions other than that of the desired user - which are likely to have different power levels - are adaptively canceled by the antenna. Finally, a payload architecture is presented that illustrates the practical implementation of this concept. This digital payload architecture demonstrates that with the advent of high performance CMOS digital processing, the on-board implementation of complex DSP techniques - in particular Digital Beamforming - has become possible, being most attractive for Mobile Satellite Communications.

Munoz-Garcia, Samuel G.↗

Black-Starting Microgrids Using Inverter-Based Distributed Energy Resources

Recent trends in power systems show increasing deployment of inverter-based distributed energy resources (DERs) at the distribution level. Efforts have also been made to use inverter-based DERs for system restoration of critical sections of distribution feeders. In the past few years, there has been active consideration of using local grid-forming (GFM) DERs, which can form their own voltage and frequency, to black-start microgrids instead of waiting for the bulk system for supply restoration. However, using current-limited inverter-based DERs for black-start operations with high inrush currents can pose some technical challenges. This paper presents detailed electromagnetic transient studies on a digital real-time simulator to assess the operational feasibility of using local inverter-based GFM DERs for microgrid black-start operations as a bottom-up approach instead of a top-down approach where the substation is used to restore critical sections of the feeder. The paper presents key modeling details for distribution system components for studying GFM DER-based microgrid black-start operations. The paper presents simulation results comparing different black-start approaches using GFM DERs for two test systems: i) a simple test microgrid and ii) a real-world utility microgrid currently being designed and deployed by Holy Cross Energy in Colorado.

black start↗

Advanced Stirling Convertor Control Unit Testing at NASA Glenn Research Center in the Radioisotope Power Systems System Integration Laboratory

Future NASA missions could include establishing Lunar or Martian base camps, exploring Jupiters moons and travelling beyond where generating power from sunlight may be limited. Radioisotope Power Systems (RPS) provide a dependable power source for missions where inadequate sunlight or operational requirements make other power systems impractical. Over the past decade, NASA Glenn Research Center (GRC) has been supporting the development of RPSs. The Advanced Stirling Radioisotope Generator (ASRG) utilized a pair of Advanced Stirling Convertors (ASC). While flight development of the ASRG has been cancelled, much of the technology and hardware continued development and testing to guide future activities. Specifically, a controller for the convertor(s) is an integral part of a Stirling-based RPS. For the ASRG design, the controller maintains stable operation of the convertors, regulates the alternating current produced by the linear alternator of the convertor, provides a specified direct current output voltage for the spacecraft, synchronizes the piston motion of the two convertors in order to minimize vibration as well as manage and maintain operation with a stable piston amplitude and hot end temperature. It not only provides power to the spacecraft but also must regulate convertor operation to avoid damage to internal components and maintain safe thermal conditions after fueling. Lockheed Martin Coherent Technologies has designed, developed and tested an Engineering Development Unit (EDU) Advanced Stirling Convertor Control Unit (ACU) to support this effort. GRC used the ACU EDU as part of its non-nuclear representation of a RPS which also consists of a pair of Dual Advanced Stirling Convertor Simulator (DASCS), and associated support equipment to perform a test in the Radioisotope Power Systems System Integration Laboratory (RSIL). The RSIL was designed and built to evaluate hardware utilizing RPS technology. The RSIL provides insight into the electrical interactions between as many as 3 radioisotope power generators, associated control strategies, and typical electric system loads. The first phase of testing included a DASCS which was developed by Johns Hopkins UniversityApplied Physics Laboratory and simulates the operation and electrical behavior of a pair of ASCs in real time via a combination of hardware and software. Testing included the following spacecraft electrical energy storage configurations: capacitive, battery, and supercapacitor. Testing of the DASCS and ACU in each energy storage configuration included simulation of a typical mission profile, and transient voltage and current data during load turn-on/turn-off. Testing for these devices also included the initiation of several system faults such as short circuits, electrical bus over-voltage, under-voltage and a dead bus recovery to restore normal power operations. The goal of this testing was to verify operation of the ACU(s) when connected to a spacecraft electrical bus.

Dugala, Gina M.↗

Multi-Agent Reinforcement Learning for Distribution System Critical Load Restoration

Grid resilience has become a critical topic recently because of the increasing occurrence of extreme events and the growing integration of intermittent renewable energy sources. To build a resilient distribution system, this paper develops a multiagent reinforcement learning-based (MARL) method to coordinate distribution energy resources (DERs) dispatch, load pickup, and network reconfiguration for load restoration after a system outage. With the help of two types of control agents, namely critical load restoration (CLR) and coordination (COR) agents, system loads can be restored efficiently, given available resources. The effectiveness and superiority of the proposed algorithm are demonstrated through simulations and comparative studies on a real distribution feeder in Western Colorado.

distribution system↗

Valuing Resilience Benefits of Microgrids for an Interconnected Island Distribution System

Extreme climate-driven events such as hurricanes, floods, and wildfires are becoming more intense in areas exposed to these threats, requiring approaches to improve the resilience of the electrical infrastructure serving these communities. Long-duration outages caused by such high impact events propagate to economic, health, and social consequences for communities. As essential service providers, electric utilities are mandated to provide safe, economical and reliable electricity to their customers. The public is becoming less tolerant to these more frequent disruptions, especially in view of technological advances that are intended to improve power quality, reliability and resilience. One promising solution is state-of-the-art microgrids and the advanced controls employed therein. This paper presents and demonstrates an approach to technoeconomic analysis that can be used to value the avoided economic consequences of grid resilience investments, as applied to the islands of Vieques and Culebra in Puerto Rico. This valuation methodology can support policies to incorporate resilience value into any investment decision-making process, especially those which serve the public interest.

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Multi-Agent Reinforcement Learning for Distribution System Critical Load Restoration: Preprint

Grid resilience has become a critical topic recently because of the increasing occurrence of extreme events and the growing integration of intermittent renewable energy sources. To build a resilient distribution system, this paper develops a multi-agent reinforcement learning-based (MARL) method to coordinate distribution energy resources (DERs) dispatch, load pickup, and network reconfiguration for load restoration after a system outage. With the help of two types of control agents, namely critical load restoration (CLR) and coordination (COR) agents, system loads can be restored efficiently, given available resources. The effectiveness and superiority of the proposed algorithm are demonstrated through simulations and comparative studies on a real distribution feeder in Western Colorado.

distribution system↗

Utilizing grid–supportive load response to shape resilient frequency control of the power grid

The increasing penetration of renewable energy sources and the retirement of conventional generation units have decreased system inertia, making power systems more vulnerable to resilience and stability issues. To address this problem, this paper proposes a novel approach using grid-supportive loads (GSLs) to provide a fast and concise primary frequency response and a deep deterministic policy gradient agent-based secondary controller to restore the system frequency to the nominal value. The proposed method is evaluated on the single-area and multi-area test systems. The simulation results demonstrate that using GSLs enhances the power system's stability and resilience. Compared to conventional controllers, the frequency nadir is improved with GSLs. Additionally, the proposed method effectively enhances resilience even with high penetration. These findings indicate that the proposed approach can improve the resilience and stability of power systems and provide a promising solution for future power systems. The results of this study underscore the importance of utilizing innovative approaches to enhance the stability and resilience of power systems in the context of high penetration of renewable energy sources and the retirement of conventional generation.

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