Power Cost Research Articles

Genome-wide association study revealed candidate genes associated with egg-laying time traits in layer chicken.

Genome-wide association study revealed candidate genes associated with egg-laying time traits in layer chicken.

Biological markers and metabolic energy indexes of show jumping horses during a field exercise test in Portugal.

Biological markers and metabolic energy indexes of show jumping horses during a field exercise test in Portugal.

Geometry‐Guided Transformer for Monocular 3D Object Detection

Monocular 3D object detection aims to identify objects’ 3D positions and poses with low hardware and computation power costs, which is crucial for scenarios like autonomous driving and deep space exploration. While the corresponding research has developed rapidly with the integration of transformer structures, features in 3D are still simply transformed from visual features, resulting in a mismatch between the detection results and the reality. Moreover, most existing methods suffer from the slow convergence speed. To address these issues in monocular 3D object detection, a framework, named geometry‐guided monocular detection with transformer (GG‐Mono), is proposed. It consists of three main components: 1) the mix‐feature encoder module that incorporates pretrained depth estimation models to enhance convergence speed and accuracy; 2) the geometry encoding module that supplements hybrid encoding with global geometry data; 3) the GG decoder module that utilizes geometry queries to guide the decoding process. Extensive experiments show that the model outperforms all existing methods in terms of detection accuracy, and achieves 26.88% and 30.65% in average precision of 3D detection box (AP3D) on the validation dataset and test dataset, respectively, which is 1.88% and 1.81% higher than the baseline, and significantly improved the convergence speed (from 184 to 90 epochs). These facts prove the advantages of the proposed method for monocular 3D object detection.

Performance of Two-Phase Designs for the Time-to-Event Outcome and a Case Study Assessing the Relapse Risk Associated With B-ALL Subtypes.

To reduce costs in genomic studies of time-to-event phenotypes like survival, researchers often sequence a subset of samples from a larger cohort. This process usually involves two phases: first, collecting inexpensive variables from all samples, and second, selecting a subset for expensive measurements, for example, sequencing-based biomarkers. Common two-phase designs include nested case-control and case-cohort designs. Additional designs include sampling subjects based on follow-up time, like extreme case-control designs. Recently an optimal two-phase design using a maximum likelihood-based method was proposed, which could accommodate arbitrary sample selection in the second phase. However, direct comparisons of this optimal design with others in terms of power and computational cost is lacking. This study performs a direct evaluation of typical two-phase designs, including Tao's optimal design, on type I error, power, effect size estimation, and computational time, using both simulated and real data sets. Results show that the optimal design had the highest power and accurate effect size estimation under the Cox regression model. Surprisingly, logistic regression achieved similar power with much lower computational cost than a more sophisticated method. The study further applied these methods to the MP2PRT study, reporting hazard ratios of cancer subtypes on relapse risk. Recommendations for selecting two-phase designs and analysis methods are regarding power, bias of estimated effect size, and computational time.

Research on Development and Analysis of Hybrid Solar Generating Module for High-Efficiency Solar Energy Station & Solar Tracking Using Arduino UNO

Abstract - The increasing demand for permanent energy solutions has increased the requirement for more efficient solar technologies. This article presents the development and evaluation of a hybrid sun beat module that integrates advanced sun tracking and energy conversion when using an Arduino UNO microcontroller. The system uses a halogen light source to simulate solar movement, which facilitates accurate traction of solar panels through a specially designed gimbal mechanism and has a tilted mirror to increase the light catch. Integration of two 6V photovoltaic panels combined with voltage sensors enables real -time monitoring of energy production, which is shown through the 16 × 2 LCD screen. The proposed system improves solar energy efficiency by maximizing photon absorption and increasing total energy production by adjusting panel orientation. Experimental results indicate that the hybrid approach leads to a significant increase of 20 to 25 percent in the energy yield compared to the static solar panel configuration, which combines active tracking with increased light reflection. In addition, the efficiency of the system can be scaled to reduce the number of modules required for power generation, cost reduction and better system contributes to flexibility. This task demonstrates the ability to adapt energy catches and integrate cost-effective, accurate tracking and reflection techniques in solar systems to optimize and promote sustainable energy solutions. Keywords- Renewable energy, hybrid model, solar energy.

Power Generation and Carbon Credit Earning by Solar Energy Systems

ABSTRACTThis paper presents a detailed analysis of power generation through renewable energy sources and the carbon credit potential of various solar energy systems installed at the Dr. Mahalingam College of Engineering and Technology (MCET), Pollachi. It consists of a solar greenhouse, solar street lights, solar pumps, and solar photovoltaic power‐generating systems. Energy generation and cost savings were achieved for these systems. Carbon credit computation was performed according to the Kyoto Protocol under local climatic conditions. The total power produced by the solar PV systems was found to be 147.456 MWh, and the total cost of power generated by these systems was found to be Rs. 12,97,613/‐ ($15540). The total annual carbon mitigation produced by the four standalone solar photovoltaic (SPAV) systems was found to be 137.42 tons, and the carbon credit earned estimated to be Rs. 5,84,432/‐ ($7008.41), whereas the annual carbon mitigation achieved through the solar greenhouse was found to be 198 tons. The annual carbon credits earned by the solar greenhouse were found to be Rs. 8.42,072/‐ ($10098), and the annual carbon credits earned by all the solar systems were found to be Rs. 14,26,504/‐ ($17106.41). The main objective of this research paper is to showcase the potential of solar systems for power generation and thermal heating applications, in addition to the benefits of CO2 mitigation. This highlights how solar systems can be developed and promoted to meet the energy requirements and environmental sustainability of every institution. The promotion of this type of clean and renewable energy source will help save the environment from the harmful effects of fossil fuels, in addition to meeting the energy requirements.

Improving the performance of glow discharge hollow cathode by using low work function materials

Abstract Hollow cathodes are widely used as neutralizers in electric propulsion. Currently, with the trend of satellite miniaturization and diversification of space missions, alternative neutralization technology based on new materials and alternative propellants has become a hot topic. This study compared the discharge characteristics and performance of different cathode materials LaB6 and 304 stainless steel in a direct-current glow discharge hollow cathode (GDHC). Numerical and experimental investigations were conducted on the inner discharge region and outer plume region of the GDHC to analyze the plasma behavior, respectively. Experimental results show that the LaB6 configuration (adding a LaB6 insert into the 304 configuration) can effectively reduce the discharge power cost and improve the gas utilization factor. An anode current of 213.6 mA was achieved at 2 sccm of argon in the LaB6 configuration, corresponding to a discharge power cost of 182 W A−1 and a gas utilization factor of 1.5. The LaB6 configuration exhibits a higher discharge current, resulting in a higher plume plasma density. The simulation results show that the electron density and ion density in the cathode fall (CF) region near the LaB6 insert increase significantly, thereby improving the electron and ion conductivity, and effectively reducing the CF voltage. Comparison reveals that the performance of the GDHC (LaB6) is comparable to RF cathodes and microwave cathodes with argon propellant.

Demand Side Response with Optimized Economic Load Dispatch using Particle Swarm Algorithm

Electrical power systems are designed and operated to meet the continuous variation of the required load demand. The literature surveyed mostly reported optimized demand response with economic dispatch without considering customer incentives for power transferred to the grid. This study seeks to explore the efficacy of evolutionary swarm optimization technique, the particle swarm optimization (PSO) to validate the optimum choice of distributed energy sources in microgrid demand side response with customer incentive-based economic load dispatch (ELD). The results indicate the cost of the grid power is directly proportional to the energy supplied and inversely proportional to the transferred energy, energy received and the customer incentives. The solar energy (P_s) and wind (P_w) supplied for hours complemented the grid supply and increased the customer incentives from $5.5 to $36 per kWh representing over 18% improvement and reduced the corresponding grid intake from 360 kWh to 317 kWh representing about 14% decrease. Future work on this demand side response with ELD should include additional optimization techniques and larger solar PV and wind energy powers to validate the efficiency in a more complex microgrid.

Research on Typical Market Mode of Regulating Hydropower Stations Participating in Spot Market

As the second largest power source in the world, hydropower plays a crucial role in the operation of power systems. This paper focuses on the key issues of regulating hydropower stations participating in the spot market. It aims at the core challenges, such as the conflict of cascade hydro plants’ joint clearing, the lack of adaptability for different types of power supply bidding on the same platform, and the contradiction between long-term operation and the spot market. Through the construction of a water spillage management strategy and settlement compensation mechanism, the competitive abandoned water problem caused by mismatched quotations of cascade hydro plants can be solved. In order to achieve reasonable recovery of the power cost, a separate bidding mechanism and capacity cost recovery model are designed. Subsequently, the sufficient electricity supply constraint of the remaining period is integrated into the spot-clearing model, which can coordinate short-term hydropower dispatch with long-term energy storage demand. The operation of the Yunnan electricity spot market is being simulated to verify the effectiveness of the proposed method.

Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System.

Occupancy monitoring (OM) and the localization of individuals within indoor environments using wearable devices offer a very promising data communication solution in applications such as home automation, smart office management, outbreak monitoring, and emergency operating plans. OM is challenging when developing solutions that focus on reduced power consumption and cost. Bluetooth low energy (BLE) technology is energy- and cost-efficient compared to other technologies. Integrating BLE Received Signal Strength Indicator (RSSI) signals with machine learning (ML) introduces a new Artificial Intelligence- (AI-) enhanced OM approach. In this paper, we propose an Intelligent Bluetooth Virtual Door (IBVD) OM system for the indoor/outdoor tracking of individuals using the interaction between a BLE device worn by the occupant and two BLE beacons located at the entrance/exit points of a doorway. ML algorithms are used to perform intelligent OM through pattern detection from the BLE RSSI signal(s). This approach differs from other technologies in that it does not require any floorplan information. The developed OM system achieves a range between 96.6% and 97.3% classification accuracy for all tested ML models, where the error translates to a minor delay in the time in which an individual's location is classified, introducing a highly reliable indoor/outdoor tracking system.

A Hybrid Stability Index and Harris Hawks Optimization for the Optimal Reallocation of Generators in the Presence of SVC

Increasing system efficiency and dependability in a very competitive power market depends on best use of available resources. This work offers a hybrid approach for generator tuning and Static VAR Compensator (SVC) placement selection to enhance system performance. The perfect generator settings are found using the Harris Hawks optimization (HHO) method; a hybrid stability index (HSI) is then used to choose the best SVC location. Measuring voltage stability, the HSI combines the Vi/Vo index with the L-index. A multi-objective function is developed to lower losses, lower the cost of power generating, and improve voltage stability. The proposed method was tested on an IEEE 30-bus system under both normal operating settings and extreme system disturbances brought on by line outages. Under both normal and fault situations, the results show the effectiveness of the HHO-based approach in raising power system stability and operational performance when compared to those obtained using the Harmony Search (HS) technique.

Enerchainability in Agriculture Supply Chain: Examining the adoption of Blockchain and its Energy Efficiency from Farmers’ Perspective

This study investigates factors influencing farmers’ acceptance of blockchain technology within the concept of “enerchainability,” which integrates energy efficiency and blockchain to enhance sustainability in the agriculture supply chain (ASC). It examines how farmers’ awareness, social networks, access to technology, and participation in agricultural training shape their behavioral intention to adopt blockchain. Data were collected through a structured survey focusing on demographic factors, social networks, and supply chain challenges. Results show that social networks, technological resources, and education significantly affect blockchain adoption. Younger, educated farmers are more receptive to the technology, while issues like reduced bargaining power and high production costs act as barriers. The study’s findings, though focused on one region, have global relevance for similar agricultural and technological contexts, offering insights into how blockchain can drive sustainability and energy efficiency. The research provides recommendations for policymakers and stakeholders, emphasizing the importance of farmer education and external support for blockchain adoption in ASC.

Self-Learning for Personalized Keyword Spotting on Ultralow-Power Audio Sensors

This paper proposes a self-learning method to incrementally train (fine-tune) a personalized Keyword Spotting (KWS) model after the deployment on ultra-low power smart audio sensors. We address the fundamental problem of the absence of labeled training data by assigning pseudo-labels to the new recorded audio frames based on a similarity score with respect to few user recordings. By experimenting with multiple KWS models with a number of parameters up to 0.5M on two public datasets, we show an accuracy improvement of up to +19.2% and +16.0% vs. the initial models pretrained on a large set of generic keywords. The labeling task is demonstrated on a sensor system composed of a low-power microphone and an energy-efficient Microcontroller (MCU). By efficiently exploiting the heterogeneous processing engines of the MCU, the always-on labeling task runs in realtime with an average power cost of up to 8.2 mW. On the same platform, we estimate an energy cost for on-device training 10× lower than the labeling energy if sampling a new utterance every 6.1 s or 18.8 s with a DS-CNN-S or a DS-CNN-M model. Our empirical result paves the way to self-adaptive personalized KWS sensors at the extreme edge.

An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks

Localization plays a crucial role in wireless sensor networks (WSNs) and it has sparked significant research interest. GPSs provide quite accurate positioning estimations, but they are ineffective indoors and in environments like underwater. Power usage and cost are further disadvantages, and so many alternatives have been proposed. Many works in the literature still base localization on RSSI measurements and often rely on methods to mitigate the effects of fluctuations in values, so it is important to know real values of RSSIs measured using common devices. This work presents the main localization techniques and exploits a real testbed to collect and evaluate RSSI measurements. An accuracy evaluation and a comparison among several localization techniques are also provided.

Impact of Load Profile on the Installation of Capacitor Banks in Conventional and Modern Distribution Grids Considering Provision Cost of Reactive Power

Impact of Load Profile on the Installation of Capacitor Banks in Conventional and Modern Distribution Grids Considering Provision Cost of Reactive Power

Ultrafast Coherence-Based Power Doppler Estimation Using Nonlinear Compounding With Complementary Subset Transmit.

Ultrafast Coherence-Based Power Doppler Estimation Using Nonlinear Compounding With Complementary Subset Transmit.

SPACE-BASED SOLAR POWER: SCIENCE FICTION OR A NEAR-FUTURE REALITY

The global energy demand is experiencing a rapid in-crease, with projections suggesting a potential doubling or 50% growth in the coming years. In response, the exploration of alter-native power generation methods has become imperative. Pho-tovoltaic power plants, while harnessing clean energy from the sun, face limitations due to their reliance on weather conditions and extensive land requirements. Space-based solar power offers a compelling alternative, providing the capa-bility to deliver continuous, carbon-free electricity with a power density exceeding that of terrestrial alternatives by more than tenfold while necessitating significantly less land.Space-based solar power systems operate by collecting and converting solar energy in space, which is then transmitted wirelessly to Earth using microwaves or lasers to generate electricity. However, achieving this po-tential requires overcoming substantial technological challenges, particularly in the domain of wireless power transmission over long distances to effectively reach ground-based receivers.As the drive towards net-zero emissions intensifies, Space-based solar power is increasingly recognized as a viable option. This paper offers an overview of Space-based solar power as an integrated system. It introduces cutting-edge advancements, including system properties and modern Space-based solar power concept archi-tectures, and examines their applications and spillover effects across various sectors. Additionally, the paper addresses the challenges and risks associated with Space-based solar power, emphasizing key barriers to suc-cessful implementation. Despite the availability of much of the requisite technology, its efficiency remains in-sufficient for practical deployment. Nevertheless, with the participation of private enterprises in the space race and ongoing advancements in system efficiency, the overall costs of Space-based solar power are decreasing. With continued progress and sustained investment, Space-based solar power, in conjunction with other renew-able technologies, holds the potential to significantly contribute to cross-sector decarbonization. Ref. 68. Fig. 9.

Smart Meter Utilizing Fuzzy Logic and IoT

Smart systems are utilized for home applications to enhance the protection reliability and cost economy. The main challenges of a smart system are time process, decision reliability, fault classification, and the ability to connect with the Internet of Things (IoT). In this proposal, a smart system power meter will be designed using artificial intelligence (AI) and IoT. The fuzzy logic with the microcontroller and the internet application are system parameters. Results show that the cost and fault of the power are successfully classified and discovered via fuzzy logic to notify the customer about the situations of home power. The system manages the home power cost and protects the home’s devices from damage.

Effect of surface plastic deformation of drill pipes material on their technological properties

Relevance. The need to study the effect of surface plastic deformation of drill pipes on their technological properties, which will significantly reduce the irrational material and energy costs of drilling wells, as well as optimize the technical and economic indicators of drilling operations. Aim. To increase the productivity of geological exploration by optimizing the operational characteristics of drill pipes. Objects. Drilling well, drilling shell, drill string, shot blasting of pipes, surface plastic deformation. Methods. Specialized test benches have been developed and manufactured for conducting experimental studies; processing of experimental research data was carried out by the graphoanalytic method. Results. The paper introduces the results of the study of the effect of surface plastic deformation produced by means of shot-impact treatment of the surface of the drill pipe material on their technological properties. The conducted studies shown a significant effect of surface plastic deformation of drill pipes on their technological properties and operational characteristics. In particular, it was found that microcracks and other microdefects can lead to a decrease in the strength and fatigue resistance of the pipe, as well as to a deterioration in its corrosion resistance. Conclusions. The optimal degree of surface coating of LBTN-54 and SBT-42 drill pipes, which guarantees a significant improvement in their technological properties, is close to 80%. With an optimal degree of surface plastic deformation of the material of LBTN-54 drill pipes the stiffness of their cross-section increases by 1.8 times; torsional stiffness increases by 1.3 times; damping capacity increases by 33%, and the amplitude of free vibrations decreases by an average of 1.9 times; power costs for turning them around their curved axis increase by 15%. At the optimal degree of surface plastic deformation of the SBT-42 drill pipe material the stiffness of their cross-section increases by 1.43 times; torsional stiffness increases by 1.4 times; damping capacity increases by 25%, and the amplitude of free vibrations decreases by an average of 1.75 times; power consumption for turning around its curved axis decreases by 7%. Surface plastic deformation of the LBTN-54 drill pipe material contributes to the expansion of the areas of their operation in the well in the mode of direct precession, which ensures the improvement of technical and economic indicators of drilling in difficult geological conditions.

Optimization of an Integrated Geothermal Energy System for Sustainable Producing Power, Cooling, and Hydrogen Storage

ABSTRACTDue to the increase in pollutants produced by fossil fuels, the earth's ecosystem has been threatened, and actions must be taken to improve the current situation. One of these actions is using renewable energies instead of fossil fuels, and another is using multigenerational production systems instead of single‐product‐oriented systems. This study investigated a geothermal system with two ejector cooling cycles combined with an improved ORC cycle with a fuel cell that can produce hydrogen by consuming production power. After performing the numerical simulation, the system's energy efficiency is 40.25%, and the exergy efficiency of the system is 22.52% for the geothermal source, which produces 6143 kW of thermal energy. The net output power of the whole system is equal to 344.1 kW, the production cooling load is equal to 2214 kW, and the hydrogen production rate is 2.369 kg/h. The amount of exergy destruction of various equipment in the cycle has been calculated and verified. Also, a parametric analysis was done based on the effect of different points' thermodynamic characteristics on the system's main parameters, such as net output power, energy and exergy efficiency, the total cost of unit power cost, sustainability index, and the results were analyzed. Also, a genetic algorithm has optimized the study cycle, and a multi‐objective optimization analysis has been presented, with the objectives of the cycle's sustainability index, exergy efficiency, and power production cost rate.