Power Constraints Research Articles

A Fully Distributed Robust MPC Approach for Frequency and Voltage Regulation in Smart Grids With Active and Reactive Power Constraints

A Fully Distributed Robust MPC Approach for Frequency and Voltage Regulation in Smart Grids With Active and Reactive Power Constraints

Maximum Transferred Power Constraints Analysis of the Grid-Tied VSC Based on the Modified Transfer Function

Maximum Transferred Power Constraints Analysis of the Grid-Tied VSC Based on the Modified Transfer Function

Ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

ABSTRACTAs 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.

Efficient Biomedical Text Summarization With Quantized LLaMA 2: Enhancing Memory Usage and Inference on Low Powered Devices

ABSTRACTThe deployment of large language models (LLMs) on edge devices and non‐server environments presents significant challenges, primarily due to constraints in memory usage, computational power, and inference time. This article investigates the feasibility of running LLMs across such devices by focusing on optimising memory usage, employing quantization techniques, and reducing inference time. Specifically, we utilise LLaMA 2 for biomedical text summarization and implement low‐rank adaptation (LoRA) quantization to compress the model size to compress the model size and fine‐tune it using limited resources. Our study systematically evaluates memory consumption during both training and inference phases, demonstrating substantial reductions through efficient LoRA quantization. Our results indicate that with careful optimization, it is feasible to deploy sophisticated LLMs like LLaMA 2 on low powered devices, thereby broadening the scope of their application in resource‐constrained environments.

An Investigation into the Viability of Cell-Level Temperature Control in Lithium-Ion Battery Packs

Abstract This article focuses on the thermal management and temperature balancing of lithium-ion battery packs. As society transitions to relying more heavily on renewable energy, the need for energy storage rises considerably, as storage facilitates power regulation between these sources and the grid. Lithium-ion batteries are leading the market for energy storage options, but their properties are temperature sensitive, with thermal abuse resulting in shortened pack lifetime and possible safety issues. Current battery thermal management systems (BTMS) are implemented in a number of ways to ensure consistent and reliable operation. However, they are typically limited in architecture and restricted in ability to attend to temperature gradients. This work proposes a BTMS topology that permits control of the individual cooling received by a cell in a pack. First, an analysis is done using timescale separation to confirm that cell-level temperature control is capable of extending the lifetime of a pack as compared to pack-level control. The analysis is used to guide the gain tuning of a state feedback controller, which directs more cooling effort to cells of higher temperatures. Validation of the BTMS topology and control is performed through the simulation of a battery pack, with variations in total cooling power and resistance heterogeneity. The outcome of the validation studies indicates that the proposed BTMS configuration is better equipped to reduce temperature differences and extend pack life. This benefit increases as total input power increases, giving the controller more freedom to cool unhealthy cells while remaining within power constraints.

UOWC spatial diversity techniques over hostile maritime environments: a new approach under imperfect CSI and per-source power constraints

UOWC spatial diversity techniques over hostile maritime environments: a new approach under imperfect CSI and per-source power constraints

Energy‐efficient power allocation and antenna selection in full‐duplex multi‐cell distributed antenna systems

AbstractEnergy efficiency problem is investigated in distributed antenna systems (DAS), in which both users and base station (BS) antennas are full‐duplex (FD) capable. Multi‐cell and multi‐user scenario is considered. Users transmit power, BS antennas transmit power, and BS antenna selection are jointly optimized under single‐antenna power constraints, per‐user power constraints, and antenna selection constraints. Using the fractional programming, we convert the objective function equivalently from the fractional form to subtractive form. Then, the reformulated problem is decomposed into two subproblems for iterative optimization, the local optimal solution is obtained. Simulation results show the energy efficiency is significantly better than other reference algorithms and close to the optimal algorithm.

Retraction Note: Solar energy harvesting to optimise the power constraints in 5G systems

Retraction Note: Solar energy harvesting to optimise the power constraints in 5G systems

Multi-objective scheduling for an energy-efficient flexible job shop problem with peak power constraint

Controlling electricity costs, which are closely related to the peak power reached, while simultaneously reducing completion time and energy consumption, is crucial for achieving sustainability in the manufacturing industry. However, most existing research on energy-efficient flexible job shop scheduling problems primarily focuses on optimizing completion time and energy consumption, often neglecting the critical aspect of controlling electricity costs. With decreasing resources and increasing energy prices, it is necessary to control electricity costs by limiting the peak power reached during production. To address this gap, this paper investigates an energy-efficient flexible job shop scheduling problem with peak power constraint considering setup and transportation time (EEFJSSP-PPST). We establish a mathematical model for EEFJSSP-PPST and propose an improved non-dominated sorting genetic algorithm II (INSGA-II) incorporating several key improvements. We first introduce three scheduling rules in decoding to optimize objectives and employ heuristic rules to generate a high-quality initial population. Then, we propose an innovative cluster crossover method to accelerate convergence and design an adaptive-based local optimization strategy to further enhance performance. Finally, our experiments explore the impacts of different degrees of the peak power constraint on objectives and evaluate the effectiveness of the three scheduling rules. Additionally, the performance of INSGA-II is tested using 135 benchmark instances. The results indicate that INSGA-II outperformed its variations without improvements, achieving the best hypervolume (HV) indicator in 80% of the instances and the best inverted generational distance (IGD) indicator in 68.89% of the instances. Compared to other state-of-the-art algorithms, INSGA-II achieves the best HV and IGD indicators in 82.2% and 81.5% of the instances, respectively, and demonstrates better convergence and a superior Pareto front. Therefore, we conclude that the improvements in INSGA-II are effective and enhance the algorithm’s performance, making it outstanding in solving EEFJSSP-PPST.

Impact of Sliding Window Variation and Neuronal Time Constants on Acoustic Anomaly Detection Using Recurrent Spiking Neural Networks in Automotive Environment

Acoustic perception of the automotive environment has the potential to advance driving potentials with enhanced safety. The challenge arises when these acoustic perception systems need to perform under resource and power constraints on edge devices. Neuromorphic computing has introduced spiking neural networks in the context of ultra-low power sensory edge devices. Spiking architectures leverage biological plausibility to achieve computational capabilities, accurate performance, and great compatibility with neuromorphic hardware. In this work, we explore the depths of spiking neurons and feature components with the acoustic scene analysis task for siren sounds. This research work aims to address the qualitative analysis of sliding windows’ variation on the feature extraction front of the preprocessing pipeline. Optimization of the parameters to exploit the feature extraction stage facilitates the advancement of the performance of the acoustics anomaly detection task. We exploit the parameters for mel spectrogram features and FFT calculations, prone to be suitable for computations in hardware. We conduct experiments with different window sizes and the overlapping ratio within the windows. We present our results for performance measures like accuracy and onset latency to provide an insight on the choice of optimal window. The non-trivial motivation of this research is to understand the effect of encoding behavior of spiking neurons with different windows. We further investigate the heterogeneous nature of membrane and synaptic time constants and their impact on the accuracy of anomaly detection. On a large scale audio dataset comprising of siren sounds and road traffic noises, we obtain accurate predictions of siren sounds using a recurrent spiking neural network. The baseline dataset comprising siren and noise sequences is enriched with a bird dataset to evaluate the model with unseen samples.

Storm Classification and Dynamic Targeting for a Smart Ice Cloud Sensing Satellite

Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a radar intelligently targets ice storms based on information collected by a look-ahead radiometer. Often, space observations are performed by continuously collecting data from an instrument aimed at nadir (e.g., directly below the space platform). However, if the platform has the ability to assess science utility of features that will be overflown, an intelligent measurement scheme can improve science return. In the case of SMICES, power constraints and the rarity of storms mean that with blind nadir targeting SMICES would collect a limited amount of ice-storm radar data. The work proposed acquires measurements to maximize acquired high-interest storms while concurrently collecting a background sampling of all features. This is accomplished through two steps: storm classification and dynamic targeting. For classification, we describe a multistep use of machine learning and digital twin of Earth’s atmosphere to create a classifier. We discuss an autonomous data labeling pipeline used to train five different models to identify storms in tropical and nontropical regions and assess the results and impact of expected noise. For dynamic targeting, six algorithms, ranging from “blind” to more selective, are described and evaluated on their improvement over “blind” targeting.

PrivacyLens: On-Device PII Removal from RGB Images using Thermally-Enhanced Sensing

Internet-connected cameras support many useful home monitoring and health applications. However, these same cameras indiscriminately capture sensitive and Personally Identifiable Information (PII), limiting their acceptance in certain settings, such as the home. Prior works removed Region of Interest (ROI) to secure images and improve privacy. However, the methods that rely solely on RGB information to find persons are susceptible to environmental and lighting conditions, causing them to fail and leak PII. From our deployment study, nearly half of the images containing persons had a PII leakage when using RGB-only methods. Furthermore, ROI removal is often performed off-device, requiring the server performing these operations to be trustworthy. This work presents the PrivacyLens system, where with the addition of thermal sensing, our system has a significantly enhanced ability to find persons in RGB images and video and efficiently remove them on the device before any data is stored or transmitted, all while staying under typical IoT power constraints. From our aforementioned deployment study in an office-building atrium, family home, and outdoor park environment, the PrivacyLens prototype effectively removes PII with a sanitization rate of 99.1%. Additionally, PrivacyLens can use its embedded GPU to generate on-device features for downstream CV/ML tasks, as shown in three illustrative applications, further reducing the collection and storage of PII.

Efficient multichannel energy harvesting with dedicated energy transmitters in CR-IoT networks

Radio Frequency (RF) energy harvesting is strongly believed to be a sustainable solution to the power depletion problem in battery powered IoT devices. In addition to harvesting energy from ambient RF signals, the use of dedicated energy transmitters (ETs) that transmit energy to nearby IoT devices via RF signals has recently been proposed. In this paper, we study the problem of designing an energy harvesting policy for a group of cognitive radio-enabled IoT (CR-IoT) devices served by a number of ETs to maximize the minimum of their charging rates. With the help of cognitive radios, a CR-IoT node is capable of changing its frequency channel of operation allowing for multi-channel energy harvesting. Frequency channels are assumed to be opportunistically accessible depending on the activity of wireless users that are licensed to use those channels. The problem entails the design of the ET’s transmission policy (to what CR-IoT device, and over what channel) and the design of an ambient harvesting policy for every CR-IoT device (when it is not served by ETs). The problem is formulated as a mixed integer linear program (MILP). The objective is to maximize a lower bound on the total harvested energy in a given time frame per CR-IoT node. This optimization is subject to scheduling, total energy budget, and maximum transmit power constraints. Given the intractability of MILP formulations, a sub-optimal algorithm is proposed. Extensive experimentation is carried out to assess the effectiveness of the proposed sub-optimal algorithm by comparing it to the MILP’s solution obtained using IBM CPLEX solver with a limit on the execution time. We also combine our sub-optimal algorithm withe the CPLEX solver to produce a new two-stages algorithm that improves the original one by around 47%. Finally, we investigate the effect of multiple parameters including number of ETs, number of channels, and channel availability probability on the minimum charging rate.

Generalized Reduced-WMMSE Approach for Cell-Free Massive MIMO With Per-AP Power Constraints

Generalized Reduced-WMMSE Approach for Cell-Free Massive MIMO With Per-AP Power Constraints

Considering the peak power consumption problem with learning and deterioration effect in flow shop scheduling

This paper investigates the permutation flow shop scheduling problem with peak power constraints under sequence-dependent setup time, learning, and deterioration effects to minimize the makespan, where the peak power consumption satisfies a given upper bound at any time. We establish relevant mathematical models based on the characteristics of the scheduling environment and set up five setup time-based heuristics, including the earliest start time, the latest setup time based on balance job–machine, latest setup time based on balance machine–job, latest setup time insert based on balance job–machine, and latest setup time insert based on balance machine–job. Similarly, a hybrid genetic algorithm combined with simulated annealing is proposed to prevent premature trapping in local optima. The algorithms are evaluated through a large number of data experiments, and the results show that it can effectively solve this scheduling problem.

Scheduling of memory chips for final testing on parallel machines considering power constraints and deteriorating effects

This paper delves into the intricate scheduling strategies crucial for the final testing phase of memory chip manufacturing within the semiconductor industry and other related sectors. It specifically addresses the complex serial-batching scheduling problem, where memory chips are tested on parallel machines under power constraints and chip deterioration effects. The processing time for each chip is significantly influenced by both the cumulative processing time of preceding chips and the power requirements for testing. We formulate this real-world optimization problem using a mixed integer nonlinear programming model. Exact solutions for small instances are obtained using a commercial solver. However, due to the model's complexity, we also develop heuristic solutions to efficiently handle larger instances. Based on the derivation of structural properties, we develop two tailored heuristic algorithms to determine the schedule for the final testing of memory chips. Additionally, we propose a refined Variable Neighborhood Search algorithm (VNS-H) that seamlessly integrates five local search strategies with two supplementary heuristic algorithms, dynamically alternating between them to ensure a balance between computational efficiency and the quality of the solutions obtained. Additionally, we establish a lower bound to validate the effectiveness of these solutions, particularly for large-scale instances. To validate the efficacy and robustness of our proposed metaheuristic algorithm, we conduct a rigorous comparison of the VNS-H algorithm with five other metaheuristic algorithms that have promising performance in various optimization problems. The results highlight the superior performance of the VNS-H algorithm. In small-scale instances, our VNS-H algorithm achieves an average makespan that is 5.52% lower compared to the original VNS. For large-scale instances, the VNS-H algorithm reduces the average makespan by 13.46% compared to VNS. Finally, we discuss the managerial implications of our findings, providing insights specifically tailored to semiconductor manufacturing enterprises based on the outcomes of this study.

Humidity-aware model predictive control for residential air conditioning: A field study

Model predictive control of residential air conditioning could reduce energy costs and greenhouse gas emissions while maintaining or improving occupants’ thermal comfort. However, most approaches to predictive air conditioning control either do not model indoor humidity or treat it as constant. This simplification stems from challenges with modeling indoor humidity dynamics, particularly the high-order, nonlinear equations that govern heat and mass transfer between the air conditioner’s evaporator coil and the indoor air. This paper develops a machine-learning approach to modeling indoor humidity dynamics that is suitable for real-world deployment at scale. This study then investigates the value of humidity modeling in four field tests of predictive control in an occupied house. The four field tests evaluate two different building models: One with constant humidity and one with time-varying humidity. Each modeling approach is tested in two different predictive controllers: One that focuses on reducing energy costs and one that focuses on constraining electric power below a utility-specified threshold. The two models lead to similar performance for reducing energy costs. Combining the results of this study and a prior heating study of the same house, the estimated year-round energy cost savings were $340–497 or 22%–31% (95% confidence intervals); these savings were consistent across both humidity models. However, in the demand response tests, the simplifying assumption of constant humidity led to far more frequent and severe violations of the power constraint.

Minimal perception: enabling autonomy in resource-constrained robots.

The rapidly increasing capabilities of autonomous mobile robots promise to make them ubiquitous in the coming decade. These robots will continue to enhance efficiency and safety in novel applications such as disaster management, environmental monitoring, bridge inspection, and agricultural inspection. To operate autonomously without constant human intervention, even in remote or hazardous areas, robots must sense, process, and interpret environmental data using only onboard sensing and computation. This capability is made possible by advancements in perception algorithms, allowing these robots to rely primarily on their perception capabilities for navigation tasks. However, tiny robot autonomy is hindered mainly by sensors, memory, and computing due to size, area, weight, and power constraints. The bottleneck in these robots lies in the real-time perception in resource-constrained robots. To enable autonomy in robots of sizes that are less than 100 mm in body length, we draw inspiration from tiny organisms such as insects and hummingbirds, known for their sophisticated perception, navigation, and survival abilities despite their minimal sensor and neural system. This work aims to provide insights into designing a compact and efficient minimal perception framework for tiny autonomous robots from higher cognitive to lower sensor levels.

Optimization and knowledge discovery of profiled end walls in a turbine stage at a low Reynolds number

To comprehensively explore flow control method of profiled end wall for turbine stage at low Reynolds numbers, a surrogate model optimization platform including non-uniform rational B-spline surface parameterization method, support vector regression, and improved chaos particle swarm optimization algorithm is integrated. Optimization designs have been carried out for stator profiled end walls, rotor profiled end wall, and combined end walls, respectively. The results indicate that under the constraint of the output power, the application of various profiled end wall design cases all can effectively improve the aerodynamic performance of the turbine stage. By organizing the flow field of downstream rotor, the profiled end wall of stator can significantly affect the stage efficiency. The flow control benefits of the profiled end wall of the rotor is from the obstruction of the cross migration of the pressure side leg of the horseshoe vortex. The application of profiled end wall on stator has the most practical engineering value. Self-organizing maps and Shapley methods are used to explore potential correlation information of aerodynamic parameters and summarize design experience. The sensitive design variables of profiled end walls are extracted. Based on the local controllability of NURBS surfaces, the regions that affect the stage efficiency are mainly concentrated in the middle of the stator passage, near the stator trailing edge and near the rotor leading edge. The regions with a significant impact on the output power of the turbine stage are near the trailing edge of the rotor and stator. The corresponding design rules of end walls modeling are summarized.

A Novel Training Design Algorithm for Kronecker-Inseparable MIMO Systems With General Power Constraints

A Novel Training Design Algorithm for Kronecker-Inseparable MIMO Systems With General Power Constraints