Optimal Planning to Include Storage Devices in Power Systems
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25 ENERGY STORAGE↗
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Europa Spacecraft Configuration Optimization for the Solar Powered Vehicle
A mission to Europa has been on the minds of NASA and JPL for many years. After the Galileo mission to Jupiter in the 1990s there have been various proposals for missions to the Jovian moon. The most recent proposal, previously named the Europa Clipper, has gone through numerous iterations of spacecraft configurations on its road to becoming an official NASA project in June of 2015. Most of these configurations included options for either multi mission radioisotope thermoelectric generators (MMRTGs) or solar power. In 2014, the decision was made to focus on solar arrays as the source for spacecraft power. The decision to move forward with a baseline design that utilized only solar arrays as its power system meant that some configuration choices had to be re- evaluated. Initially, a configuration was adapted to keep as much of the previous spacecraft design the same while replacing MMRTGs with solar panels. This proved to be difficult as the arrays presented a slew of new challenges that the nuclear vehicle was not optimized for. The solar arrays needed to be large due to Jupiter's substantial distance from the Sun. This meant that many of the instrument and radiator FOVs would now be obstructed, or would receive reflected light and heat from the large panel s. Also, the mass of the panels meant that mounting near the bottom of the spacecraft would be sub-optimal as the wings would cause major disturbance to the vehicle as they oscillated in their deployed state. Another major, and possibly the largest, concern was the fact that as the high gain antenna pointed to Earth for communication, the Ice Penetrating Radar (IPR) would cast a large shadow on the cell -side of the array. This resulted in an estimated 10% power loss to the vehicle. On top of all this, NASA announced the selection of the instruments that would fly on the Europa mission and replace the notional instrument suite that had been used to develop and submit the project proposal. The selected instruments, while not varying widely from the notional suite, did come with a new set of challenges including a size increase over the notional package, thus requiring more room for accommodation. They also introduced new features not previously addressed by the notional package, such as a two-axis gimbal on one of the imagers. Additionally, two new instruments, an ultraviolet plume -hunting spectrograph, and an atmospheric dust analyzer we added to the payload and presented new challenges not previously covered in the proposal. Finally, additional payloads were under consideration, such as a 250kg ejectable payload that would be released at Jupiter and would accomplish flybys of some of the other Jovian moons. All of this resulted in a drastically different "family" of configurations that were capable of addressing these issues, and staying flexible to the numerous potential changes that could come. This paper discusses the details of the various configurations considered to address these items, and the configuration concepts that were selected as the baseline for moving forward with the proposal.
Horner, Matthew D.↗
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
As we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical and challenging. We propose a low-overhead autotuning framework to autotune performance and energy for various hybrid MPI/OpenMP scientific applications at large scales and to explore the tradeoffs between application runtime and power/energy for energy efficient application execution, then use this framework to autotune four ECP proxy applications—XSBench, AMG, SWFFT, and SW4lite. Our approach uses Bayesian optimization with a Random Forest surrogate model to effectively search parameter spaces with up to 6 million different configurations on two large-scale HPC production systems, Theta at Argonne National Laboratory and Summit at Oak Ridge National Laboratory. The experimental results show that our autotuning framework at large scales has low overhead and achieves good scalability. Using the proposed autotuning framework to identify the best configurations, we achieve up to 91.59% performance improvement, up to 21.2% energy savings, and up to 37.84% EDP (energy delay product) improvement on up to 4096 nodes.
Autotuning↗
State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin
Real-time state estimation using a digital twin can overcome the lack of in-field measurements inside an electric feeder to optimize grid services provided by distributed energy resources (DERs). Optimal reactive power control of DERs can be used to mitigate distribution system voltage violations caused by increased penetrations of photovoltaic (PV) systems. In this work, a new technology called the Programmable Distribution Resource Open Management Optimization System (ProDROMOS) issued optimized DER reactive power setpoints based-on results from a particle swarm optimization (PSO) algorithm wrapped around OpenDSS time-series feeder simulations. This paper demonstrates the use of the ProDROMOS in a RT simulated environment using a power hardware-in-the-loop PV inverter and in a field demonstration, using a 678 kW PV system in Grafton (MA, USA). The primary contribution of the work is demonstrating a RT digital twin effectively provides state estimation pseudo-measurements that can be used to optimize DER operations for distribution voltage regulation.
Darbali-Zamora, Rachid↗
Europa lander mission concept power architecture
The Europa Lander mission concept team seeks to optimize a power system architecture to search for evidence of life on the surface of Europa, an icy moon of Jupiter. Environmental and mission challenges included radiation, extreme low temperatures, planetary protection, mission duration, and the limitations of the launch vehicle. Leveraging heritage from NASA’s Europa Clipper mission, the baseline architecture is highly reliable, capable of meeting all science objectives, and comprises of three vehicles: a solar powered carrier stage, a secondary battery powered descent vehicle, and a primary battery powered lander.
Carr, Gregory↗
DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions
Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high $R/X$ ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.
42 ENGINEERING↗
Design of a high power TM01 mode launcher optimized for manufacturing by milling
Recent research on high-gradient rf acceleration found that hard metals, such as hard copper and hard copper-silver, have lower breakdown rate than soft metals. Traditional high-gradient accelerating structures are manufactured with parts joined by high-temperature brazing. The high temperature used in brazing makes the metal soft; therefore, this process cannot be used to manufacture structures out of hard metal alloys. In order to build the structure with hard metals, the components must be designed for joining without high-temperature brazing. One method is to build the accelerating structures out of two halves, and join them by using a low-temperature technique, at the symmetry plane along the beam axis. The structure has input and output rf power couplers. We use a TM01 mode launcher as a rf power coupler, which was introduced during the Next Linear Collider (NLC) work. The part of the mode launcher will be built in each half of the structure. This paper presents a novel geometry of a mode launcher, optimized for manufacturing by milling. The coupler was designed for the CERN CLIC working frequency f = 11.9942 GHz; the same geometry can be scaled to any other frequency.
36 MATERIALS SCIENCE↗
Performance optimization of an MHD generator with physical constraints
A technique has been described which optimizes the power out of a Faraday MHD generator operating under a prescribed set of electrical and magnetic constraints. The method does not rely on complicated numerical optimization techniques. Instead the magnetic field and the electrical loading are adjusted at each streamwise location such that the resultant generator design operates at the most limiting of the cited stress levels. The simplicity of the procedure makes it ideal for optimizing generator designs for system analysis studies of power plants. The resultant locally optimum channel designs are, however, not necessarily the global optimum designs. The results of generator performance calculations are presented for an approximately 2000 MWe size plant. The difference between the maximum power generator design and the optimal design which maximizes net MHD power are described. The sensitivity of the generator performance to the various operational parameters are also presented.
Pian, C. C. P.↗
A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks
As a result of climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks; thus, the level of proactive preparation of the distribution system to be able to handle severe impacts of extreme weather events represents the system's resilience. One method that distribution system planners can use to prepare for future extreme events is to plan multiple microgrids which can use local generation as much as possible to supply critical loads. But partitioning an existing distribution system such that multiple feasible islands are planned and which are capable of supporting critical loads is still challenging for distribution systems - first, because of the size of the network graph partitioning problem and, second, because of the difficulty in properly formulating the desired attributes of such islands or microgrids. Therefore, this paper presents a genetic algorithm based approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems - node allocation and edge elimination - and it considers multiple topological and resilience-enhancing objectives. The performance of the proposed genetic algorithm-based approach is numerically evaluated on multiple test systems as well as on a real distribution feeder in Colorado, United States.
genetic algorithm↗
A hybrid Monte Carlo and response matrix Monte Carlo method in criticality calculation
Full core calculations are very useful and important in reactor physics analysis, especially in computing the full core power distributions, optimizing the refueling strategies and analyzing the depletion of fuels. To reduce the computing time and accelerate the convergence, a method named Response Matrix Monte Carlo (RMMC) method based on analog Monte Carlo simulation was used to calculate the fixed source neutron transport problems in repeated structures. To make more accurate calculations, we put forward the RMMC method based on non-analog Monte Carlo simulation and investigate the way to use RMMC method in criticality calculations. Then a new hybrid RMMC and MC (RMMC+MC) method is put forward to solve the criticality problems with combined repeated and flexible geometries. This new RMMC+MC method, having the advantages of both MC method and RMMC method, can not only increase the efficiency of calculations, also simulate more complex geometries rather than repeated structures. Several 1-D numerical problems are constructed to test the new RMMC and RMMC+MC method. The results show that RMMC method and RMMC+MC method can efficiently reduce the computing time and variations in the calculations. Finally, the future research directions are mentioned and discussed at the end of this paper to make RMMC method and RMMC+MC method more powerful. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Optimized Swinging Door Algorithm for Wind Power Ramp Event Detection: Preprint
Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA.
17 WIND ENERGY↗
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↗
Nuclear Power Fault Diagnostics and Preventative Maintenance Optimization
Operation and maintenance costs for nuclear power plants are very large. Reactors are starting to shut down even after their operating licenses have been extended, because they are not price competitive compared to other energy sources. The nuclear industry is witnessing early closure of nuclear power plants due to economic reasons despite excellent safety records. Therefore, it is imperative to reduce costs to prevent these early closures. This paper showcases recent research into advanced fault diagnostics techniques and preventative maintenance optimization to reduce these maintenance costs. This report focuses on the condensate and feedwater system for both pressurized and boiling water reactor systems. The computerized maintenance management system, which contains the plant’s digital record of all the corrective- and preventative-maintenance work orders, was used as a ground truth to locate potential faults and label the process data as healthy or faulty. Various feature extraction techniques were utilized to further differentiate the faults from the healthy data. Support vectors machines were used to categorize other test sets of process data as healthy or faulty through a cross validation procedure. Similar faults were not found within this system leading to preventative maintenance optimization. Unnecessary amounts of preventative maintenance lead to inflated maintenance costs. This paper summarizes the steps for preventative maintenance optimization from component health determination to recommendation for action. This optimization was completed for condensate pumps, condensate booster pumps, and the respective motors that drive them.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Advanced Transmission Technologies – GETs and HPCs Session 2: Advanced Power Flow Control and Transmission Topology Optimization
The INL TADA GETs Cohort Session 2, held on November 7, 2025, conducted in collaboration with ScottMadden, focused on two core Advanced Transmission Technologies (ATTs): Advanced Power Flow Control (APFC) and Transmission Topology Optimization (TTO). These technologies are pivotal in enhancing grid flexibility, reliability, and cybersecurity resilience. APFC, particularly through modular FACTS devices like Modular Static Synchronous Series Compensators (M-SSSCs), enables dynamic voltage injection to reroute power flows. The session highlighted the deployment benefits of APFC, such as rapid installation, minimal civil works, and re-deployability. Regulatory drivers like FERC Order 2023 mandate the inclusion of Grid-Enhancing Technologies (GETs) in interconnection studies. Case studies from Central Hudson, CAISO, and National Grid (UK) demonstrated APFC’s effectiveness in congestion relief and cost savings. The session also addressed cybersecurity concerns, including firmware vulnerabilities, SCADA integration risks, and supply chain dependencies. Participants engaged in interactive exercises to rank cybersecurity and supply chain risks, emphasizing the need for robust digital assurance strategies. TTO involves software-based reconfiguration of transmission networks to optimize power flow without new infrastructure. The session showcased its operational value, with examples from SPP, PJM, and MISO showing significant congestion cost reductions. Cybersecurity vulnerabilities were discussed, particularly in API security and software supply chains, referencing incidents like SolarWinds and attacks on Danish utilities. Digital assurance exercises explored worst-case scenarios, attack paths, and mitigation responsibilities between vendors and utilities. Reliability challenges such as algorithm stability, vendor dependency, and operator trust were also examined. Cross-cutting themes emphasized the importance of digital assurance tools, including Software Bills of Materials (SBOMs) and hardware-in-loop testing. Human performance, training, and operational confidence were identified as critical enablers of technology adoption. The session concluded with a preview of Session 3, which will focus on High Performance Conductors (HPCs) and risk-based cybersecurity tools. Session 2 of 3.
24 - POWER TRANSMISSION AND DISTRIBUTION↗
Probabilistic N - k failure-identification for power systems
This work considers a probabilistic generalization of the N - k failure-identification problem in power transmission networks, where the probability of failure of each component in the network is known a priori and the goal of the problem is to find a set of k components that maximizes disruption to the system loads weighted by the probability of simultaneous failure of the k components. The resulting problem is formulated as a bilevel mixed-integer nonlinear program. Convex relaxations, linear approximations, and heuristics are developed to obtain feasible solutions that are close to the optimum. A general cutting-plane algorithm is proposed to solve the convex relaxation and linear approximations of the N - k problem. Extensive numerical results corroborate the effectiveness of the proposed algorithms on small-, medium-, and large-scale test instances; the test instances include the IEEE 14-bus system, the IEEE single-area and three-area RTS96 systems, the IEEE 118-bus system, the WECC 240-bus test system, the 1354-bus PEGASE system, and the 2383-bus Polish winter-peak test system.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Repurposing an energy system optimization model for seasonal power generation planning
Not Available
de Queiroz, A. R.↗
Recursive restoration refinement: A fast heuristic for near-optimal restoration prioritization in power systems
Not Available
Rhodes, Noah (ORCID:0000000257707221)↗
Running from features: Optimized evaluation of inflationary power spectra
Not Available
Motohashi, Hayato↗