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At least 271 records · Page 15

Formulation of a methodology for power circuit design optimization

A methodology for optimizing power-processor designs is described which achieves optimization with respect to some power-processor characteristic deemed particularly desirable by the designer, such as weight or efficiency. Optimization theory based on Lagrange multipliers is reviewed together with nonlinear programming techniques employing penalty functions. The methodology, the task of which is to minimize an objective function subject to design constraints, is demonstrated with the aid of four examples: optimum-weight core selection for an inductor with a predetermined winding size, optimum-weight inductor design with a given loss constraint, optimum-loss inductor design with a given weight constraint, and a comparison of optimum-weight single- and two-stage input-filter designs with identical loss and other requirement constraints. Closed-form solutions for the first three examples are obtained by applying the Lagrange-multiplier method, but solutions for the last example are found numerically through the use of the sequential unconstrained minimization technique.

Yu, Y.↗

Unraveling the fundamental mechanisms of solvent-additive-induced optimization of power conversion efficiencies in organic photovoltaic devices

The realization of controllable morphologies of bulk heterojunction (BHJ) in organic photovoltics (OPVs) is one of the key factors in obtaining high-efficiency devices. Here via simultaneous monitoring of the three-dimensional nanostructural modifications in BHJ correlated with the optical analysis and theoretical modeling of charge transport, we provide new insights into the fundamental mechanisms essential for the optimization of (power conversion efficiency) PCEs with additive processing. Our results demonstrate how a trace amount of diiodooctane (DIO) remarkably changes the vertical phase morphology of the active layers resulting in formation of a well-mixed donor-acceptor compact film, augments charge transfer and PCEs. In contrast, excess amount of DIO promotes a massive reordering and results loosely packed mixed phase vertical phase morphology with large clusters leading to deterioration in PCEs. Theoretical modeling of charge transport reveals that DIO increases the mobility of electrons and holes (the charge carriers) by affecting the energetic disorder and electric field dependence of the mobility. Our results show the significant of phase separation and carrier transport pathways to achieve optimal device performances.

14 SOLAR ENERGY↗

Multi-Objective Optimization for Power Electronics used in Grid-Tied Energy Storage Systems

Multi-objective optimization is used to find a nondominated set of solutions for two conflicting performance metrics or objective functions. These functions are dependent variables in the system, controlled by a set of independent variables called decision variables. The decision variables represent the inputs to the problem, chosen by the system designer, and are values listed in the solution set. In this study, a multi-objective genetic algorithm compared insulated-gate bipolar transistor (IGBT) failure rate to filter and cooling system costs. This study demonstrated the use of multi-objective optimization for energy storage systems. IGBT failure rate was compared to its associated filter and cooling-system costs as part of the DC-AC inverter power electronics system in a battery energy storage system (BESS). The independent or decision variables were determined to be switching frequency and thermal resistance of a heat sink, R sink . The final results indicated that high values of switching frequency increased the effects of R sink . Future work will add additional objective functions and decision variables to the study to optimize additional components in the power electronics system and BESS.

25 ENERGY STORAGE↗

Linear solvers for power grid optimization problems: A review of GPU-accelerated linear solvers

The linear equations that arise in interior methods for constrained optimization are sparse symmetric indefinite, and they become extremely ill-conditioned as the interior method converges. These linear systems present a challenge for existing solver frameworks based on sparse LU or LDL T decompositions. Here, we benchmark five well known direct linear solver packages on CPU- and GPU-based hardware, using matrices extracted from power grid optimization problems. The achieved solution accuracy varies greatly among the packages. None of the tested packages delivers significant GPU acceleration for our test cases. For completeness of the comparison we include results for MA57, which is one of the most efficient and reliable CPU solvers for this class of problem.

97 MATHEMATICS AND COMPUTING↗

Maximal nighttime electrical power generation via optimal radiative cooling

We present a systematic optimization of nighttime thermoelectric power generation system utilizing radiative cooling. We show that an electrical power density >2 W/m 2 , two orders of magnitude higher than the previously reported experimental result, is achievable using existing technologies. This system combines radiative cooling and thermoelectric power generation and operates at night when solar energy harvesting is unavailable. The thermoelectric power generator (TEG) itself covers less than 1 percent of the system footprint area when achieving this optimal power generation, showing economic feasibility. We study the influence of emissivity spectra, thermal convection, thermoelectric figure of merit and the area ratio between the TEG and the radiative cooler on the power generation performance. We optimize the thermal radiation emitter attached to the cold side and propose practical material implementation. The importance of the optimal emitter is elucidated by the gain of 153% in power density compared to regular blackbody emitters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

HydraGNN_OPF_GFM_2026 - Ensemble of predictive graph foundation models for power grid applications

This dataset supports research on graph foundation models for optimal power flow (OPF) on electric grids using HydraGNN. It contains heterogeneous graph representations of PGLib-OPF cases spanning systems from 14 to 13,659 buses, together with packed HDF5 datasets for pretraining, feasibility classification, and N-1 contingency analysis. The release includes OPF solution data, downstream fine-tuning datasets, pretrained HeteroSAGE and HeteroHEAT model checkpoints, hyperparameter-optimization summaries across multiple heterogeneous GNN architectures, and aggregated fine-tuning results for sample-efficiency studies. The dataset is designed to enable scalable training, evaluation, and transfer-learning studies for OPF surrogate modeling, including node-level AC-OPF solution prediction, graph-level prediction, feasibility classification, operating-condition generalization, and contingency-response tasks.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Estimating the value of jointly optimized electric power generation and end use: a study of ISO-scale load shaping applied to the residential building stock

A generation-to-load simulation estimated the impact, in terms of production costs and CO2 emissions, attributable to the joint optimization of electric power generation and flexible end uses to support increasing penetrations of renewable energy. Newly conceived, evaluated, and foundational in developing a U.S. National Standard was a transaction-less yet continuous demand response system based on a day-ahead optimum load shape (OLS) designed to encourage Internet-connected devices to autonomously and voluntarily explore options to favour lowest cost generators - without requiring two-way communications, personally identifiable information, or customer opt-in. Boundary conditions used for model calibration included historical weather, residential building stock construction attributes, home appliance and device empirical operating schedules, prototypical power distribution feeder models, thermal generator heat rates, startup and ramping constraints, and fuel costs. Results of an hourly-based annual case study of Texas indicate a 1/3 reduction in production costs and a 1/5 reduction in CO2 emissions are possible.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A Three-Stage Enhanced Reactive Power and Voltage Optimization Method for High Penetration of Solar

This paper presents a three-stage enhanced volt/var optimization method to stabilize voltage fluctuations in transmission networks by optimizing the usage of reactive power control devices. In contrast with existing volt/var optimization algorithms, the proposed method optimizes the voltage profiles of the system, while keeping the voltage and real power output of the generators as close to the original scheduling values as possible. This allows the method to accommodate realistic power system operation and market scenarios, in which the original generation dispatch schedule will not be affected. The proposed method was tested and validated on a modified IEEE 118-bus system with photovoltaic data.

photovoltaic, Volt/Var Control, indexes, simulatio↗

Concentrating Solar Power (CSP) Plant Optimization Study for the California Power Market (CalCSP)

In the United States, many states are implementing goals to achieve 100% carbon free power generation by 2050 or sooner. California has one of the more aggressive goals to achieve 100% carbon free generation of its retail power sales by 2045. California has excellent solar resources, but to accomplish this goal, California’s power utilities will need new zero carbon resources that can replace the natural gas units that they currently rely on for suppling power at night. Appropriately configured concentrating solar power plants with thermal energy storage are an option to serve this nighttime load. These plants would be designed to take advantage of CSP’s low-cost thermal energy storage and collect and store energy during the day and then be dispatched to produce power at night. The U.S. Department of Energy has funded a study to identify the design of a CSP plant that optimally meets the evolving CA grid needs. This will be done by taking a fresh look at how CSP technologies can best be designed to meet the emerging nighttime market for carbon free power generation or zero-carbon firm resources. The paper provides an overview of the study and present preliminary findings on the California power market requirements, CSP configurations, technoeconomic analysis, siting opportunities, and key issues for CSP deployment in this market.

Price, Hank (ORCID:0000000267049197)↗

Manual of phosphoric acid fuel cell power plant optimization model and computer program

An optimized cost and performance model for a phosphoric acid fuel cell power plant system was derived and developed into a modular FORTRAN computer code. Cost, energy, mass, and electrochemical analyses were combined to develop a mathematical model for optimizing the steam to methane ratio in the reformer, hydrogen utilization in the PAFC plates per stack. The nonlinear programming code, COMPUTE, was used to solve this model, in which the method of mixed penalty function combined with Hooke and Jeeves pattern search was chosen to evaluate this specific optimization problem.

Lu, C. Y.↗

Stochastic Modeling and Power Control of Time-Varying Wireless Communication Networks

Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.

Doppler Power Spectral Density↗

Doubling fusion power with volumetric optimization in magnetic confinement fusion devices

A technique, volumetric power optimization, is presented for enhancing the power output of magnetic confinement fusion devices. Applied to a tokamak, this approach involves shifting the burning plasma region to a larger plasma volume while introducing minimal perturbations to the plasma boundary shape. This edge perturbation—squareness—is analogous to pinching and stretching the edge boundary. Stability calculations confirm that this edge alteration is compatible with maintaining plasma stability. This optimization method for optimizing fusion power output could improve the performance of magnetic confinement fusion power plants.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then we reformulate and linearize the models into a mixed integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a twosettlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.

electricity market↗

CPES-QSM: A Quantitative Method Towards the Secure Operation of Cyber-Physical Energy Systems

Power systems are evolving into cyber-physical energy systems (CPES) mainly due to the integration of modern communication and Internet-of-Things (IoT) devices. CPES security evaluation is challenging since the physical and cyber layers are often not considered holistically. Existing literature focuses on only optimizing the operation of either the physical or cyber layer while ignoring the interactions between them. This paper proposes a metric, the Cyber-Physical Energy System Quantitative Security Metric (CPES-QSM), that quantifies the interaction between the cyber and physical layers across three domains: electrical, cyber-risk, and network topology. A method for incorporating the proposed cyber-metric into operational decisions is also proposed by formulating a cyber-constrained AC optimal power flow (C-ACOPF) that considers the status of all the CPES layers. The C-ACOPF considers the vulnerabilities of physical and cyber networks by incorporating factors such as voltage stability, contingencies, graph-theory, and IoT cyber risks, while using a multi-criteria decision-making technique. We note that simulation studies are conducted using standard IEEE test systems to evaluate the effectiveness of the proposed metric and the C-ACOPF formulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Scenario Creation and Power-Conditioning Strategies for Operating Power Grids with Two-Stage Stochastic Economic Dispatch

A significant difficulty associated with the use of stochastic programming to solve optimal power flow problems on a 5-minute timescale is the quality of renewable energy scenarios input by the user. This is especially true when considering power systems with high penetrations of renewable energy, e.g. wind power. This paper introduces the use of stochastic programming to solve the DC optimal power flow problem with scenarios drawn directly from high-fidelity data sets. Hence, the proposed method avoids the problem of lost physics by finding high-fidelity analogs that can describe future states of the system. Furthermore, this method can be simply extended to output multi-period scenarios to the stochastic program. We demonstrate the effectiveness of this technique by simulating dispatch operations on a synthetic test system over the course of a week.

94 GMLC - Grid Modernization Laboratory Consortium↗

Scenario Creation and Power-Conditioning Strategies for Operating Power Grids with Two-Stage Stochastic Economic Dispatch

A significant difficulty associated with the use of stochastic programming to solve optimal power flow problems on a 5-minute timescale is the quality of renewable energy scenarios input by the user. This is especially true when considering power systems with high penetrations of renewable energy, e.g. wind power. This paper introduces the use of stochastic programming to solve the DC optimal power flow problem with scenarios drawn directly from high-fidelity data sets. Hence, the proposed method avoids the problem of lost physics by finding high-fidelity analogs that can describe future states of the system. Furthermore, this method can be simply extended to output multi-period scenarios to the stochastic program. We demonstrate the effectiveness of this technique by simulating dispatch operations on a synthetic test system over the course of a week.

analog forecast↗