Optimal Power Research Articles

Neural network‐based integrated reactive power optimization study for power grids containing large‐scale wind power

AbstractThe high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network‐based comprehensive reactive power optimization method for large‐scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind‐power‐load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind‐power‐load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision‐making time of reactive power optimization are reduced.

Dynamic Power Saving for CMOS Circuits

With more functionalities being integrated into a microchip today, higher processing power is drawn. As a result of this, clock and logic power consumption has turned out to be a critical issue to be coped with by chip designers. In this paper, we present various power-saving approaches employed in complementary metal oxide semiconductor (CMOS) circuit designs. The approaches involve restructuring the logic circuits, performing clock gating, and selecting the appropriate circuits for counters and frequency divisions. In order to show their efficacies in power optimization, the approaches were applied to a phase-locked loop (PLL), clock divider (CD), full adder (FA), counter, arithmetic logic unit (ALU), and microprocessor without interlocked pipelined stages (MIPS) circuits and validated using Intel Quartus Prime Lite and Mentor Graphics Modelsim. The following conclusions can be drawn from the results: Firstly, the efficacy of minimizing power dissipation using logic restructuring is found to be in direct proportion with the rate of the switching activity (SA); secondly, a maximum of 3.5% of thermal power dissipation can be saved using clock gating; thirdly, gray counters give the lowest power consumption; and, finally, the thermal power estimation for the phase-locked loop (PLL) is relatively higher than that for the clock divider (CD) when both of them are implemented for dividing frequencies.

QoS-Aware Power-Optimized Path Selection for Data Center Networks (Q-PoPS)

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable.

Optimum Solar Cell Power Conversion Efficiency-Based Patterned Planar Solar Surface Morphology with Various Solar Cell Absorber Structures

This paper simulated the optimum solar cell power conversion efficiency-based planar solar surface morphology with various solar cell absorber structures. The spectral light intensity, reflected, absorbed, and transmission coefficients versus wavelength band spectrum varied from 300[Formula: see text]nm to 1300[Formula: see text]nm through the visible to near-infrared regions. The optimum solar cell performance parameters or key parameters are studied with different solar cell structure designs based on different surface contact and back contact layers. Cumulative generation current, cumulative electron–hole pair generation rate and electron–hole pair generation rate are clarified versus substrate depth for various solar cell structure designs. The absorbed current in substrate is optimized for various solar cell structure designs. Maximum power voltage and current are also optimized for the proposed solar cell structure design. The effective solar cell power efficiency is also demonstrated based on the optimized maximum power current and maximum power voltage.

3-D curved composite façade elements with PV: Results of a pilot project

This paper describes the design, manufacturing, installation and monitoring of a BIPV system that consists of coloured 3D structured façade elements, mounted on an existing office building at the High Tech Campus in Eindhoven. The façade elements contain thin film CIGS modules, which adopt the curved structure of the elements. The encapsulation of the PV modules into the façade elements, the colouration and the curvature led to a relative loss of about 10 % of the efficiency. The PV façade consists of 48 elements with a STC capacity of 5.8 kWp. Each element was equipped with a power optimizer, enabling individual monitoring of each façade element. The elements have been installed at the east and south façade of the building. The monitoring presented in this paper comprised a period of 5 months: the summer and autumn of 2023. The DC performance ratio during that period was 75 % for the elements on the East façade and 80 % for the elements on the South façade; which is in accordance with simplified model predictions. The result is a visual attractive retrofitted PV façade contributing to zero emission strategy of the Eindhoven High Tech Campus.

PEA-PODs: Perceptual Evaluation of Algorithms for Power Optimization in XR Displays

Display power consumption is an emerging concern for untethered devices. This goes double for augmented and virtual extended reality (XR) displays, which target high refresh rates and high resolutions while conforming to an ergonomically light form factor. A number of image mapping techniques have been proposed to extend battery usage. However, there is currently no comprehensive quantitative understanding of how the power savings provided by these methods compare to their impact on visual quality. We set out to answer this question. To this end, we present a perceptual evaluation of algorithms (PEA) for power optimization in XR displays (PODs). Consolidating a portfolio of six power-saving display mapping approaches, we begin by performing a large-scale perceptual study to understand the impact of each method on perceived quality in the wild. This results in a unified quality score for each technique, scaled in just-objectionable-difference (JOD) units. In parallel, each technique is analyzed using hardware-accurate power models. The resulting JOD-to-Milliwatt transfer function provides a first-of-its-kind look into tradeoffs offered by display mapping techniques, and can be directly employed to make architectural decisions for power budgets on XR displays. Finally, we leverage our study data and power models to address important display power applications like the choice of display primary, power implications of eye tracking, and more 1 .

An Approach for Hybrid Optimal Power Flow Strategies with Optimal Allocation of Distributed Generators for a Power Network

The OPF difficulties are characterized by the presence of four distinct objective functionswhich are: active powerlosses,fuel operating costs and voltage variations. The volatile and variable nature of distributed generator (DG) sources contributes to the convolutionof the optimum power flow (OPF) issue. This paperemployed bat algorithms to tackle the issue of optimumpower flow (OPF)and PSO algorithm to optimal allocation of DG. The goal was to enhance the effectiveness of energy use, environmental sustainability, and cost efficiency of the electricity grid. The test cases include a wide range of scenarios, including the existence of Distributed Generation (DG), different locations of DG within the network, various kinds of load expansion, and occasional breakdowns of transmission lines. These possibilities are considered in order to address the problem presented by the suggested solution OPF. The incidents are assessed utilizing bus systems, such as the 30 bus systemin MATLAB.

Evaluating the influence of working parameters on the efficiency of a solid oxide fuel cell by conducting sensitivity analysis using electrochemical and thermodynamic modeling

AbstractThis research article presents an investigation conducted through a numerical model to analyze the influence of various operational parameters on the performance of solid oxide fuel cells (SOFCs). The parameters studied include operating temperature, current density, pressure, steam‐to‐carbon ratio, and fuel utilization. The electrochemical model employed the Butler‐Volmer equation, Fick's model, and Ohm's law to calculate concentration, activation, and ohmic losses. The primary focus was on evaluating the generated power and electrical efficiency as performance metrics. The study revealed that increasing operating temperature and pressure resulted in higher power generation and specific optimum points were identified for optimal SOFC operation. Notably, the highest power generated was 812 kW, achieved at an operating temperature of 950 K and a current density of 18100 A/m2. Additionally, decreasing the fuel utilization factor to 55% at 15250 A/m2 led to a power output of 706 kW. Similarly, at a current density of 17150 A/m2 and a pressure of 400 kPa, the fuel cell generated about 780 kW of power. Furthermore, the research demonstrated that reducing the steam‐to‐carbon ratio increased power generation, with an optimum power output of 704 kW achieved at a current density of 16000 A/m2 and a low steam‐to‐carbon ratio. Notably, this point also showcased the improved electrical efficiency of the solid oxide fuel cell. Overall, this study underscores the significance of specific operational factors that significantly impact SOFC performance. By comprehending these parameters, it becomes possible to enhance the utilization of solid oxide fuel cells across various applications.

Power optimization of high-power random Raman laser with a full-open cavity.

Since the concept of distributed feedback fiber random laser was put forward, random Raman fiber laser (RRFL) has made great progress in high power operation. For RRFL with a full-open cavity, the simplest cavity structure, further power scaling was restricted by the rapid increase of high-order Stokes wave. In this paper, we demonstrate that the output power of the RRFL can be further improved by optimizing the fiber length. The relationship between the RRFL output power and fiber length is researched theoretically and experimentally. Results show that to optimize the RRFL with a full-open cavity in output power to the best, the fiber length should be as short as possible, under the premise of avoiding causing strong four-wave mixing (FWM) and ensuring the sufficient absorption of signal light.

Advances in the Design of Renewable Energy Power Supply for Rural Health Clinics, Case Studies, and Future Directions

Globally, effective and efficient healthcare is critical to the wellbeing and standard of living of any society. Unfortunately, several distant communities far from the national grid do not have access to reliable power supply, owing to economic, environmental, and technical challenges. Furthermore, unreliable, unavailable, and uneconomical power supply to these communities contributes significantly to the delivery of substandard or absence of qualitative healthcare services, resulting in higher mortality rates and associated difficulty in attracting qualified healthcare workers to the affected communities. Given these circumstances, this paper aims to conduct a comprehensive review of the status of renewable energy available to rural healthcare clinics around the globe, emphasizing its potential, analysis, procedures, modeling techniques, and case studies. In this light, several renewable energy modeling techniques were reviewed to examine the optimum power supply to the referenced healthcare centers in remote communities. To this end, analytical techniques and standard indices for reliable power supply to the isolated healthcare centers are suggested. Specifically, different battery storage systems that are suitable for rural healthcare systems are examined, and the most economical and realistic procedure for the maintenance of microgrid power systems for sustainable healthcare delivery is defined. Finally, this paper will serve as a valuable resource for policymakers, researchers, and experts in rural power supply to remote healthcare centers globally.

Achievable Rate Analysis and Power Optimization for Cell-Free Massive MIMO URLLC Systems Over Aging and Correlated Channels

Achievable Rate Analysis and Power Optimization for Cell-Free Massive MIMO URLLC Systems Over Aging and Correlated Channels

An efficient solution for large offshore wind farm power optimization with the Porté-Agel wake model: Optimality and efficiency

The wake effects may cause significant energy loss in the large offshore wind farm without proper coordination. Therefore, cooperative yaw and induction control is needed to mitigate wake effects and increase power output. This study reveals that conventional methods to solve the power optimization problem may trap into local optimum and have slow computation speed for large offshore wind farm. In this paper, the Porté-Agel wake model is adopted to precisely capture the dynamics and a data-driven method is proposed to calibrate its parameters only using measurements. Then, we propose a distributed hybrid search-BFGS(DHSB) algorithm to solve the power optimization problem. In this algorithm, firstly the yaw angles of some specific turbines are searched coarsely and distributionally to provide a good initial point and escape from the local optimum. Then, a distributed BFGS is developed to solve the optimization problem with very high efficiency, in which the gradient and objective value are updated in distributed manner. Simulation results show that our proposed DHSB can always achieve global optimum with much high efficiency compared to traditional gradient-based methods.

Specific Investigations Concerning the Improveable Multiplier Architecture of High-Speed and Area-Efficient Adders

Over the past few decades, the growth of portable devices such as laptops, mobile phone and personal digital assistant has resulted in increasing demand for complex functionality with effective computation. The present day technology is known for digital systems with very high computing capabilities. The demand for high speed, low power integrated circuits for portable devices has become crucial. The never ending growing complexities of integrated circuit for future devices pose a challenging task for integrated circuit designer. Cost effective integrated circuits requires the design meeting out the challenging task to optimize power, area with high performance. Hence this research focuses on the optimization of area, power and speed of Arithmetic circuits, Very Large Scale Integrated circuits (VLSI) are widely used in Arithmetic circuits, Digital Signal Processing (DSP), Image and Video Processing applications

TYtool: A software application for yield and target power optimization in charged particle induced radionuclide production

A software application useful for optimization and maximizing radionuclide production without exceeding the power limits of the target material or holder has been developed. The software (TYtool) can be used for target optimization including yield calculations vs time, thickness, and incident energy, W/mm through the target, and automated target thickness optimization with the Makkonen-Craig & Helariutta − or the novel Yield/Wapproach. TYtool also facilitates graphical representation of many basic plots such as cross section, mass stopping power, activity output, projected range and it is useful for many different particles, reactions, radionuclides via the default cross section data base or the import function for cross section and mass stopping power. The software is available for free at: https://gitlab.com/NM_Karolinska_Solna/TYtool.

A coordinated active and reactive power optimization approach for multi-microgrids connected to distribution networks with multi-actor-attention-critic deep reinforcement learning

As a promising approach to managing distributed energy, the use of microgrids has attracted significant attention among those managing continuous connections to distribution networks. However, the barriers of the data sharing among different microgrids, the uncertainty of the distributed renewable sources and loads, and the nonlinear optimization of power flow make traditional model-based optimization methods difficult to be applied. In this paper, a data-driven coordinated active and reactive power optimization method is proposed for distribution networks with multi-microgrids. A multi-agent deep reinforcement learning (MADRL) method is used to protect the data privacy of each microgrids. Moreover, attention mechanism, which pays attention to crucial information, is presented to overcome the problem of slow convergence caused by the dimensionality explosion of the optimized variables. Two types of agents, controlling discrete action and continuous action devices, respectively, are formulated in coordinated optimization, which reduces voltage violations and improves the system operation efficiency. In addition, in order to improve the performance of the online agent model under variable operation conditions, the transfer learning is embedded in the training process of the MADRL. The proposed method is verified on a modified IEEE 33-bus distribution network with nine microgrids.

An artificial gorilla troops optimizer for stochastic unit commitment problem solution incorporating solar, wind, and load uncertainties.

The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.

Joint Optimization Algorithm for Latency-Energy and Privacy Combining Discrete Differential Evolution and Convex Optimization

A privacy protection task offloading algorithm combining discrete differential evolution and convex optimization is proposed to resolve the new position privacy leakage problem for mobile terminal due to transferring tasks to multiple edge servers for computation via wireless networks. First, the privacy cost of offloading decision is defined for the characteristics of the position privacy problem combined with the standard deviation theory. Second, the objective problem is modeled as a joint optimization of position privacy protection, offloading decision and transmission power, and the total cost of task offloading is an optimization objective. Finally, to handle the objective problem efficiently, offloading decision variable is decoupled from the transmission power variable to decrease the computational complexity of the problem; the discrete differential evolution algorithm is designed to accumulate the approximate optimal offloading decision, and the optimal transmission power is efficiently solved by variable substitution method and subgradient method. Simulations reveal that the algorithm has excellent convergence; it improves the position privacy protection level of mobile terminal and reduces the total cost of task offloading while maintaining lower energy consumption and latency.

Wind Power Station Efficiency Increase Methods Based on Wind Turbine Condition Process Mathematical Modelling when Distributing Electric Power Between the Consumers in Arctic and Far North Regions

The relevance of wind power plant efficient control method based on wind turbine condition process mathematical modeling is proved for electric power distribution between consumers, intended to minimize decision-making time on technological process redistribution of electric power production for another consumer by means of actual information on equipment failure time (wind turbine, electric power stocking system), that provides optimum power production and consumption planning in northern regions. The review of the basic mathematical modeling methods of electric power redistribution between consumers that showed there are certain problem statements with insufficient descriptions of mathematical models or the absence of ready and effective methods to solve them, in particular, wind turbine condition process mathematical modeling for electric power distribution to consumers in northern regions. As a result of the analysis of wind power plant efficient control method on the basis of wind turbine condition process mathematical modeling to distribute electric power to consumers in northern regions game theory and pulse system functioning mathematical methods application based on difference equations is expedient. The decision-making strategies stipulated by 'nature' (working capacity or wind turbine failure), the scheduler (repair or wind turbine redistribution) and a condition of phase co-ordinate (repair, idle, redistribution or operation wind turbine modes) are defined. On the basis of the accepted strategy and use of step function Heaviside F(t) for fixing of technological process time, the description of the wind turbine condition equation was made providing operative failure time information access on because due to idle time, redistribution or repair that provides optimum electric power production planning for a consumer.

Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach

Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach

Impact of pre and post-perovskite infiltration treatments on monolithic perovskite solar cell performance

Carbon-based monolithic perovskite solar cells (mPSCs) represent an enticing frontier in the domain of organic–inorganic hybrid solar cells, capturing substantial research attention due to their cost effectiveness and straightforward fabrication process. Despite these merits, the challenge of achieving uniform pore filling in mPSCs, especially within mesoporous layers comprising titania, zirconia, and carbon alongside perovskite, persists. The uncontrolled and confined crystallization of the perovskite precursor within these mesoporous layers warrants meticulous investigation. This study addresses the issues related to uncontrolled crystallization by employing temperature-assisted infiltration techniques spanning from room temperature to 70 °C across triple mesoporous scaffolds. Devices were intricately fabricated using a semi-automatic drop-casting procedure, incorporating a (5-AVA)x(MA)1−xPbI3-mixed cation perovskite. Following infiltration, comprehensive pore filling of oxide layers was achieved through chlorobenzene-assisted antisolvent treatment. Comparisons were made between chlorobenzene-assisted and untreated samples under ambient conditions and thermal stress (40–70 °C). The temperature-dependent effects on perovskite infiltration and recrystallization were systematically investigated through dark and light current–voltage (J–V) characteristics, Impedance Spectroscopy (IS), and X-ray Diffraction (XRD) analyses. The findings revealed that the optimum power conversion efficiency (PCE) of 13.34% was attained when perovskite infiltration occurred at 40 °C with antisolvent treatment. Dark J–V and IS results indicated that temperature-assisted infiltration not only stimulated charge transfer but also effectively suppressed recombination. Under chlorobenzene treatment, XRD peaks exhibited broadening, indicating a reduction in perovskite crystallite size. This phenomenon facilitated the development of perovskite crystals across all available mesoporous spaces, leading to an enhanced interface property conducive to efficient charge transfer. The insights gleaned from this study on the controlled crystallization of perovskite precursors within mesoporous layers hold significant promise for advancing the stability and efficiency of mPSCs.