Power Source Research Articles

LCA analysis of a roof mounted PV system: a Romanian case study

Using solar photovoltaic power sources has become a discussed topic in the construction and energy industry. The pressing need to reduce reliance on fossil fuels, increasing costs of traditional electricity generation, and affording photovoltaic modules has sparked a growing interest in solar photovoltaics. This study aims to optimis e the layout of solar photovoltaic systems to minimise environmental impact and building load, comparing the performing south-oriented panels with east-west-oriented panels. The comprehensive analysis performed hinges on a diverse array of determinative factors that demand thoughtful consideration before embarking upon implementing any photovoltaic installation. These salient factors, including but not limited to structural integrity, incorporating ballast for stability enhancement, integrating requisite electrical components, selecting solar panels, quantifying energy production capabilities, assessing carbon emissions, and discerning associated benefits, require thoughtful consideration before implementing any photovoltaic installation. The findings derived from this study underscore that, within the context of the given geographical location, the solar photovoltaic system configured with an East-West orientation represents the optimal choice to reduce both emissions and structural load. This study provides a scientific basis for the construction industry and the energy field and guides the future development of photovoltaic installation projects in a more economic and environmentally friendly direction.

Performance Analysis of MPBC with PI and Fuzzy Logic Controllers Applied to Solar Powered Electric Vehicle Application

One of the more complicated cases is managing the energy between multiple power sources that are utilized to power electric cars (EVs). Power management is often carried out following the load requirements of electric vehicles. By considering the speed and current values of the motor a novel controller is modeled named as Measurement of parameter-based controller (MPBC) which is used to obtain the smooth transition between two passive energy sources battery and Supercapacitor (SCap). Further, the proposed MPBC is combined with fuzzy logic (FLC) and proportional-integral (PI) controllers, forming two different hybrid controllers named MPBC+FLC and MPBC+PI, utilized to attain proper power management. The main function of traditional controllers FLC/PI is to generate the pulse signals to the switches present in the bidirectional converters at both battery and SCap end. On the other hand, the MPBC is utilized to control the pulse signals based on the current and speed values of the electric motor. Futcher's final MATLAB/Simulink model is built with the proposed control technique with two hybrid controllers by considering different power-generating conditions of the PV array, to know the effectiveness of the individual model.

Solar Operated Car Enabled with AI

The convergence of renewable energy and artificial intelligence (AI) has spurred a transportation revolution, exemplified by the rise of solar-powered cars equipped with advanced AI capabilities. This abstract digs into the integration of solar energy and AI technology in autos, examining their synergistic potential to transform the automotive industry and pave the way for a more sustainable future.The paper presents the concept of solar-powered cars and discusses their importance in reducing environmental concerns connected with regular fossil-fuelled vehicles. Solar energy, captured by solar panels built into the vehicle's construction, provides a clean and abundant power source, minimizing reliance on non-renewable resources and lowering greenhouse gas emissions. Furthermore, advances in solar panel efficiency and energy storage technology have increased the practicality of solar-powered vehicles for widespread use.. Furthermore, AI-powered autonomous driving capabilities improve safety by reducing human error and allowing for proactive collision avoidance methods. The paper highlights how AI-enabled solar-powered vehicles have the potential to completely change the automotive sector and hasten the shift to a sustainable transportation network

Microgrid F36ault Detection Method Based on Lightweight Gradient Boosting Machine–Neural Network Combined Modeling

The intelligent architecture based on the microgrid (MG) system enhances distributed energy access through an effective line network. However, the increased paths between power sources and loads complicate the system’s topology. This complexity leads to multidirectional line currents, heightening the risk of current loops, imbalances, and potential short-circuit faults. To address these challenges, this study proposes a new approach to accurately locate and identify faults based on MG lines. Initially, characteristic indices such as fault voltage, voltage fundamentals at each MG measurement point, and extracted features like peak voltage values in specific frequency bands, phase-to-phase voltage differences, and the sixth harmonic components are utilized as model inputs. Subsequently, these features are classified using the Lightweight Gradient Boosting Machine (LightGBM), complemented by the bagging (Bootstrap Aggregating) ensemble learning algorithm to consolidate multiple strong LightGBM classifiers in parallel. The output classification results of the integrated model are then fed into a neural network (NN) for further training and learning for fault-type identification and localization. In addition, a Shapley value analysis is introduced to quantify the contribution of each feature and visualize the fault diagnosis decision-making process. A comparative analysis with existing methodologies demonstrates that the LightGBM-NN model not only improves fault detection accuracy but also exhibits greater resilience against noise interference. The introduction of the bagging method, by training multiple base models on the initial classification subset of LightGBM and aggregating their prediction results, can reduce the model variance and prevent overfitting, thus improving the stability and accuracy of fault detection in the combined model and making the interpretation of the Shapley value more stable and reliable. The introduction of the Shapley value analysis helps to quantify the contribution of each feature to improve the transparency and understanding of the combined model’s troubleshooting decision-making process, reduces the model’s subsequent collection of data from different line operations, further optimizes the collection of line feature samples, and ensures the model’s effectiveness and adaptability.

Pentingnya Pendidikan Demokrasi Untuk Membentuk Sikap dan Prilaku Demokratis

Democracy is very important for the life of the nation and state. Democracy teaches us that the people are the source of power and that they create good rules and protect their rights. However, currently there are many violations of democracy. Many people do not understand democratic attitudes and behavior, which causes inequality. Society is not yet mature enough to follow and respond to the democratic process due to a lack of socialization and education about democracy. The purpose of writing this journal is to provide an understanding of the Importance of Democratic Education to Form Democratic Attitudes and Behavior. This research method uses the library method or literature study. The results obtained show that democratic education has the ability to provide education and help solve the problems of the nation and state. Democratic education instills democratic principles so that it can form a society whose behavior reflects democratic life.

Tidal range energy conversion from Benoa, Bali

Abstract As one of the oceanographic parameters, the tidal range can be used as a source of blue energy in producing renewable energy such as electricity in Indonesian territory, especially in small islands. Utilization of tidal ranges in producing electrical energy will be more environmentally friendly when compared to Steam-Electric Power Station, which require coal resources as a power source. This research aims to determine the potential for renewable energy from tides in Benoa waters, Bali. The data used in this study is sea level data from tide gauges sourced from the University of Hawaii Sea Level Center (UHSLC). Analysis of tidal types and components in this study was carried out using the least squares method using the R program language. The results showed that the tidal type of Benoa waters is a mixed-semidiurnal tide. In 2020 and 2022, the highest tidal ranges are 2.46m (in December) and 2.3m (in March, November, and December), respectively. The simulation results for a 1000 m2 pond show that the energy potential in 2020 is 24.0 kWh and in 2022 it is 27.7 kWh. The tidal value’s magnitude influences the tidal energy potential; the more significant the tidal value, the greater the energy produced.

A Novel Fit-Flexible Fluorescence Soft Imager: Tri-Sensing of Intensity, Fall-Time, and Life Profile.

Time-resolved fluorescence imaging techniques, like confocal fluorescence lifetime imaging microscopy, are powerful photonic instrumentation tools of modern science with diverse applications, including: biology, medicine, and chemistry. However, complexities of the systems, both at specimen and device levels, cause difficulties in quantifying soft biomarkers. To address the problems, we first aim to understand and model the underlying photophysics of fluorescence decay curves. For this purpose, we provide a set of mathematical functions, called "life models", fittable with the real temporal recordings of histogram of photon counts. For each model, an equivalent electrical circuit, called a "life circuit", is derived for explaining the whole process. In confocal endomicroscopy, the components of excitation laser, specimen, and fluorescence-emission signal as the histogram of photon counts are modelled by a power source, network of resistor-inductor-capacitor circuitry, and multimetre, respectively. We then design a novel pixel-level temporal classification algorithm, called a "fit-flexible approach", where qualities of "intensity", "fall-time", and "life profile" are identified for each point. A model selection mechanism is used at each pixel to flexibly choose the best representative life model based on a proposed Misfit-percent metric. A two-dimensional arrangement of the quantified information detects some kind of structural information. This approach showed a potential of separating microbeads from lung tissue, distinguishing the tri-sensing from conventional methods. We alleviated by 7% the error of the Misfit-percent for recovering the histograms on real samples than the best state-of-the-art competitor. Codes are available online.

Too hot to be innovative: Examining the relationship between high temperatures and corporate green innovation

A substantial body of literature suggests that climate change-induced extreme weather events, including high temperatures, can affect country-level economic output and growth. However, there is still a lack of knowledge about how extreme weather events affect the organizational moves, particularly their responses in the environmental domain, such as corporate green innovation. Building upon the attention-based view, we argue that high temperatures may lead to operational disruptions which require a shift in managerial attention, thereby impeding corporate green innovation. We have substantiated the negative impact of high temperatures on corporate green innovation based on the analysis of a sample of Chinese publicly listed manufacturing firms. Furthermore, we find that the top management team with green or technical backgrounds, two critical sources of informal expertise power, enables firms to redirect their attention and prioritize corporate green innovation, thereby mitigating the negative impact of high temperatures.

Hybrid renewable/grid power systems, an essential for base transceiver station penetration in Rural Nigeria

The energy crisis in Nigeria has continued to impede the rapid expansion of the telecommunication industry, whose operating expenditure is galloping due to over-dependence on diesel generators as an alternative source of power to its base transceiver station (BTS). This fossil-fuel power source has also increased the industry’s carbon footprint. As a solution to these problems, the objective of this work is to provide a sustainable and quality hybrid DC power supply system for BTS that would increase access to information and communication technology or ICT infrastructure. This involves the integration of solar & wind energy with the grid. The sizing of the hybrid sub-systems was designed & simulated using MATLAB Simulink to test for functionality. A prototype of the design system was then implemented with the results showing an average power output that guarantees 21 hours/day of supply. By installing this hybrid system of 1.3 kW, approximately 2.55 kg of diesel (C10H20) would be un-utilized by one BTS, thereby preventing 3.6 kg of CO2 from been emitted to the atmosphere daily. Extrapolating these values shows 930.75 kg of diesel can be saved and reduce 1314 kg of CO2 emission within a year. Hence eliminating the need for diesel-backup generator for a grid connected or non-grid BTS sited in rural areas.

Fuzzy-based pelican optimization for battery management and energy scheduling in solar aided electric vehicle

Electric vehicles (EVs) are considered an essential mode of transportation due to their advantages, non-polluting and noise-free characteristics. The batteries are considered the primary power source for electric vehicles, ensuring energy supply and thus improving the user experience. However, designing charging stations and uncontrolled charge scheduling are the barriers evaluated in charging stations. To solve these issues, a fuzzy-based pelican optimization algorithm (POA) is proposed in this work. Set of rules are generated in the fuzzy for optimal energy management inn charging station. POA schedules the EVs in the charging station based on arrival time and SOC to reduce the running cost. Demand in the charging station is based on the state of charge (SOC) of the EVs arriving at the charging station. Solar power with battery and ultracapacitor is used as an input source for charging stations. The energy management in the system is based on fuzzy to satisfy the load demand from PV and energy storage devices. This proposed method is implemented in the Simulink tool to evaluate the performance. This simulation result show the effectiveness of the proposed with optimal scheduling and energy management. By this method running cost are reduced compared to the existing algorithms.

Bifunctional polyoxometalate clusters-modified single-walled carbon nanotubes for high-energy–density micro-supercapacitors

The rapid advancement of wearable electronics has pushed the urgent demand for flexible power sources with high energy density and fast integrations. Herein, we created high-energy–density micro-supercapacitors (MSCs) based on phosphomolybdic acid (PMo12) clusters anchored single-walled carbon nanotubes (SWCNTs) via the electrostatic assembly. The SWCNT microelectrodes for MSCs were directly fabricated by an aerosol process without any solvent-based treatment. The PMo12 clusters were found to not only increase the electrical conductivity of SWCNT thin-film electrodes, but also offer significant pseudo-capacitance of MSCs. The PMo12-modified SWCNTs MSCs demonstrated an areal capacitance of 10.2 mF cm−2 when scanned at 5 mV s−1, coupled with a notable energy density of 0.71 μWh cm−2 at a power density of 7 μW cm−2, being much higher than the one without PMo12 modification. The density functional theory simulation further reveals the enhancement of electrical conductivity and areal capacitance induced by PMo12 modifications. Moreover, our strategy is highly scalable for integrations via series or parallel connections, which could deliver higher voltage output or areal capacitance. This work demonstrates great potential for high-performance MSCs toward wearable electronics applications.

Design and verification of q-axis perturbation based active islanding detection schemes for DG systems

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.

A winding design for improving 3-phase induction motor performance

One of the most popular electric motors used today is the 3-phase induction motor, which has a sturdy design, is less expensive, and is simple to use. Improvements to the materials used in the rotor or stator of induction motors, raising the number of motor phases, and employing a 3-phase induction motor for 1-phase power are just a few of the ways the motor is now being developed to perform better. These studies are all pricey, though. The goal of this study is to determine how to enhance the motor's performance at a reasonable cost. The suggested remedy was to create a 3-phase induction motor winding with a 1-layer design that resembled a symmetrical 6-phase winding. The primary study topics were the motor's rotor speed, mechanical torque, efficiency, and winding current when it was powered by a three-phase power source. The results of the study show that, although consuming less winding current, the 3-phase induction motor with a new winding design outperforms a traditional 3-phase induction motor in terms of rotor speed, mechanical torque, and efficiency.

Microgrid based on ICA and improved cuckoo search algorithm optimizing scheduling methods

Abstract The optimal operation of microgrids can provide a strong guarantee for the efficient utilization of distributed power and renewable energy. To solve the important problem of optimized operation of microgrids with a large proportion of distributed power sources, a microgrid optimal scheduling and operation method is proposed in the paper. This method first establishes a microgrid model containing distributed power sources and uses the ICA algorithm to screen out the key factors in the economy of the grid system. Then, taking the construction and operation cost of the microgrid as the objective function, it proposes to solve the model based on the ICA and the optimized cuckoo search algorithm. The method is able to effectively schedule the microgrid objective function based on the conditions, with the ability to quickly solve the scheduling problem and greatly reduce the system’s electricity cost. It can greatly increase the utilization rate of photovoltaic power generation. The method has a small amount of computation, small storage, and simple operation. An empirical study is carried out on the designed microgrid optimal scheduling scheme.

High Ion-Conducting PVA Nanocomposite Hydrogel-Based Wearable Piezoelectric and Triboelectric Sensors for Harsh Environments.

Traditional hydrogel-based wearable sensors with flexibility, biocompatibility, and mechanical compliance exhibit potential applications in flexible wearable electronics. However, the low sensitivity and poor environmental resistance of traditional hydrogels severely limit their practical application. Herein, high-ion-conducting poly(vinyl alcohol) (PVA) nanocomposite hydrogels were fabricated and applied for harsh environments. MXene ion-conducting microchannels and poly(sodium 4-styrenesulfonate) ion sources contributed to the directional transport of abundant free ions in the hydrogel, which significantly improved the sensitivity and mechanical-electric conversion of the nanocomposite hydrogel-based piezoelectric and triboelectric sensors. More importantly, the glycerol as an antifreezing agent enabled the hydrogel-based sensors to function in harsh environments. Therefore, the nanocomposite hydrogel exhibited high gauge factor (GF) at -20 °C (GF = 3.37) and 60 °C (GF = 3.62), enabling the hydrogel-based sensor to distinguish different writing letters and sounding words. Meanwhile, the hydrogel-based piezoelectric and triboelectric generators showed excellent mechanical-electric conversion performance regardless of low- (-20 °C) or high- (60 °C) temperature environments, which can be applied as a visual feedback system for information transmission without external power sources. This work provides self-powered nanocomposite hydrogel-based sensors that exhibit potential applications in flexible wearable electronics under harsh environmental conditions.

Evaluation of electric vehicle response capability in power grid

In recent years, frequent and substantial area-wide power outages have underscored the critical need for cities to possess robust backup power sources capable of swift response to prevent prolonged power system disruptions. Electric vehicles can contribute electricity to the power grid using vehicle-to-grid technology. The power delivered by electric vehicles in this context is termed as response capability. However, existing studies have overlooked response capability dynamics during transitions between electric vehicle states—such as the shift from charging or discharging to an idle state, thereby hindering a comprehensive understanding of this aspect. Hence, this paper introduces a multi-timescale response capability prediction model that evaluates the electric vehicle's state of charge to ensure users’ requirements are met for upcoming trips. To better assess users' travel demand, the gravity model is employed as a precursor to response capability prediction to further enhance the validity of the prediction outcomes. Three neighborhoods in Los Angeles have been chosen for analysis: Downtown, Lincoln Heights, and Silver Lake. Predictions indicate that neglecting the response capability when electric vehicles undergo state transformation can lead to a differential response capability ranging from 2000 kWh to 4000 kWh, resulting in a loss of prediction accuracy by 20 % to 25 %.•The response capability of EV is non-zero during state transformations•Users’ travel demand assessment•Seamless integration of vehicle-to-grid technology into the power grid.

Potential-Regulated Design for Direct Recycling of Degraded LiFePO4 Cathode.

The rapid proliferation of power sources equipped with lithium-ion batteries poses significant challenges in terms of post-scrap recycling and environmental impacts, necessitating urgent attention to the development of sustainable solutions. The cathode direct regeneration technologies present an optimal solution for the disposal of degraded cathodes, aiming to non-destructively re-lithiate and straightforwardly reuse degraded cathode materials with reasonable profits and excellent efficiency. Herein, a potential-regulated strategy is proposed for the direct recycling of degraded LiFePO4 cathodes, utilizing low-cost Na2SO3 as a reductant with lower redox potential in the alkaline systems. The aqueous re-lithiation approach, as a viable alternative, not only enables the re-lithiation of degraded cathode while ignoring variation in Li loss among different feedstocks but also utilizes the rapid sintering process to restore the cathode microstructure with desirable stoichiometry and crystallinity. The regenerated LiFePO4 exhibits enhanced electrochemical performance with a capacity of 144mA h g-1 at 1 C and a high retention of 98% after 500 cycles at 5 C. Furthermore, this present work offers considerable prospects for the industrial implementation of directly recycled materials from lithium-ion batteries, resulting in improved economic benefits compared to conventional leaching methods.

Scenario of availability of power sources in Uttar Pradesh agriculture

Scenario of availability of power sources in Uttar Pradesh agriculture

Intelligent Light Emitting Diode Flashing Supplementary Lighting Control Design and Its Big Data Illumination Analysis

Considering the inherent advantages of the Light Emitting Diode (LED) in the field of illumination, this work designs an intelligent supplementary lighting system using LED as the light source. Combining microcontroller and electronic circuit theory, the circuit is built with the microcontroller PIC16F873 as the core control chip. The system utilizes an external 220 V AC-20 V DC conversion power supply, hence operating on a 20 V DC power source. The system consists of four hardware parts: the onboard power supply uses TI-produced TPS54331 as the control chip to achieve voltage conversion; the external signals (flashing and burst flashing signals) are isolated from the microcontroller through an optocoupler circuit; the PWM pulses output from the microcontroller’s RC1/CCP2 pins drive the corresponding switching tubes to achieve the flashing function; the flashing synchronization signal is output externally after optocoupler isolation, and its synchronous output with the flashing signal is achieved through an optocoupler after LED conduction; the circuit is established using TI-produced differential bus transceiver SN65LBC184D to convert the external 485 differential signal to the level signal required by the microcontroller. In the experiment, after completing the hardware design, connecting the LED panel, and debugging the test program, it is found that the designed lighting system has a good supplementary lighting effect. According to the PWM output waveform, the flashing effect meets the design expectations. The Hadoop big data computing platform is introduced. Simulation testing reveals that under no backlight conditions, the system achieves an illumination intensity of around 20 klx at a distance of about 10 meters. With backlight conditions, the system maintains an illumination intensity of around 1.5 klx at a distance of about 10 meters. Further calculations are performed to analyze the variation in foot traffic within the test area’s illumination over 24 hours. The total illumination intensity during different time intervals is compiled, confirming that the system can autonomously adjust the illumination intensity of the area based on changes in foot traffic.

Feasibility analysis of virtual clean hydrogen plant platform: A case study on energy policy in S. Korea using stochastic programming

In S. Korea, high-density renewable and high-density load areas are separate. This separation has led to increased transmission constraints. As both Renewable Energy Source (RES) penetration and power demand simultaneously increase, RES saturation occurs. This phenomenon reduces both the benefits of power transactions and the acceptance of RES. Addressing this issue requires actions from Independent Power Producers (IPP) and policymakers. This paper suggests a Virtual Clean Hydrogen Plant (VCHP) platform design that consolidates the Photovoltaic (PV) energy from high-density renewable areas into a single power plant. The platform converts excess energy, generated by PV due to transmission constraints, into hydrogen and supplies it to the high-density load areas. The South Korean government plans to introduce a Clean Hydrogen Portfolio Standard (CHPS), which offers a fixed premium (FP) to private power generators that provide power through clean hydrogen. Consequently, the proposed benefit structure of the VCHP platform includes an FP, settled through CHPS. This paper's design and operation algorithm for the VCHP platform incorporates stochastic programming to analyze PV operation and deterministic optimization for the optimal scheduling of Fuel Cells (FC) that provide power and heat to high-density load areas. The simulation results demonstrate that the minimum subsidy required for CHPS benefits varies depending on how PV-coupled P2G technology is utilized. Specifically, engaging in net metering with VCHP over the course of one month can establish a cost-effective benefit structure, verified to require a subsidy ranging from a minimum of 0.2036 $/kWh to a maximum of 0.2416 $/kWh.