Power Distribution Research Articles

Redefining artificial lighting through spectral engineering of light sources for well-being

Light-emitting diodes (LEDs) have revolutionized artificial lighting, but also exposed the detrimental health effects that stem from insufficient exposure to natural light. Human-centric artificial lighting requires both visual quality and circadian lighting performance that mimics daylight’s evolving spectral power distribution (SPD). Here, we present a color-tunable LED-based light source that achieves SPDs similar to various conditions of daylight and incandescent lighting over the range of visible wavelengths. This light source is comprised of a linear combination of light converter channels containing dyes exhibiting thermally activated delayed fluorescence (TADF) fabricated through additive manufacturing, photoexcited by violet-emitting LEDs (VLED). This hybrid light source establishes a new benchmark for state-of-the-art artificial lighting at approximating daylight, with Illuminating Engineering Society (IES) color gamut index Rg values within 3%, IES color fidelity index Rf values within 7% through CCT values ranging from 4277 K to 22,333 K. We propose efficiency metrics to accurately quantify similarity between light sources and the respective reference daylight spectrum encompassing visual and circadian effects, facilitating WLED benchmarking. The efficiency metrics pertaining to circadian lighting performance remain within 10% over the same CCT range. These results advance lighting science to address simultaneously the grand challenges of health and sustainability.

AI-optimization operation of biomass-based distributed generator for efficient radial distribution system

This research aims to optimize the size and location of biomass-based distributed generator (BMDG) units to enhance the voltage profile, reduce electrical losses, maximize cost savings, and decrease emissions from power distribution systems. Biomass-based distributed generator (BMDG) systems offer numerous advantages to enhance the efficiency of power distribution systems. However, achieving these benefits relies on determining the optimal size and position of the BMDGs. To achieve these objectives, the metaheuristic technique called particle swarm optimization (PSO) is employed to find the optimal placement and size of BMDGs. The proposed model was validated on MATLAB's IEEE-33 bus radial distribution system (RDS), confirming the aforementioned benefits. Comparative analysis between the PSO-based technique and other algorithms from previous research revealed better results with the proposed method. The results indicate that optimal placement and sizing of BMDG units have led to a reduction of more than 67.68% in active power losses and 65.90% in reactive power losses compared to the base case. Additionally, the reduction in active power loss was 40.44%, 11.39%, 42.85%, 1.81%, 0.85%, 29.83%, 5.82% and 28.38% more than artificial bee colony, backtracking search optimization algorithm, moth-flame optimization, Coordinate control, artificial Hummingbird algorithm, variable constants PSO (VCPSO), artificial gorilla troops optimizer (AGTO), and a jellyfish search optimizer respectively. Furthermore, the reactive power losses were reduced by 38.33% and 15.68% compared to VCPSO and AGTO respectively. Furthermore, this study revealed a cost reduction of 6.38% when compared to the AGTO and 1.30% when compared to the AHA. Moreover, the voltage profile of the power distribution system was improved by 7.28%. The presented methodology has demonstrated promising results for BMDGs in RDS across various applications.

Overload Alarm Prediction in Power Distribution Transformers

The growing demand for electricity puts more strain on the grid, requiring automated and proactive strategies such as overload prediction to improve grid maintenance. However, the intermittent nature of power distribution loads makes the prediction more challenging. This paper proposes a novel framework for overload alarm prediction in distribution transformers, aimed at enhancing the reliability and efficiency of grid operations. Leveraging real-world smart meter data and machine learning techniques, the proposed system develops a classification model to predict overloads for distribution transformers. Due to resource constraints, a new strategy is adopted to assess the significance of alarms based on expert observations. Subsequently, a new approach is developed to imitate the experts, leading to an automated decision-making process using random forest. Ultimately, the transfer learning strategy is utilized to predict overload alarms for distribution transformers facing data scarcity in real-world applications. The proposed system demonstrates high accuracy of overload alarm predictions, paving the way for developing more proactive grid maintenance strategies.

Photodamage prediction model and optimal lighting system for polychrome artworks in museum

When lighting polychrome artworks in museum, the light sources are required not only to provide high-quality visual performance, but also to minimize the photodamage. Currently, though there are mature methods to calculate visual performance, how to predict the degree of photodamage remains a challenge, which leads to a lack of light sources satisfying both visual and protection requirements. Our study conducted a series of 294 accelerated aging experiments spanning over 11 years for materials commonly used in Chinese polychrome artworks, using isoenergetic white-light D55 and 10 different wavelengths of narrow-band light as experimental light sources. The mathematical model predicting the photodamage degree caused by intensity (I), exposure time (t), the relative spectral power distribution of the light source (S(λ)), and the irradiated material's relative spectral responsivity (P(λ)) to polychrome artworks was established, and the mean absolute percentage error of the model, as verified by experiments, was 11.63 %. Combined with the existing visual performance calculation methods, an optimal lighting algorithm that simultaneously meets high-quality visual performance and minimum photodamage was developed, followed by developing a new Spectrally Tunable White LEDs (STWLEDs) lighting system with 10 LED chips. It is found that the STWLEDs can adjust and output the optimal spectra and recommended illuminance according to the characteristics of polychrome artworks, thus realizing lighting with minimum photodamage, meanwhile, satisfying high-quality visual performance. After testing, as well as satisfying the requirements of visual performance, the average photodamage is reduced by 40.95 % with STWLEDs compared with the traditional WLEDs.

Theoretical Investigation of Rhodium-Decorated Gallium Nitride Nanotubes for Sulfur Hexafluoride Decomposition Products Sensing and Scavenging Applications.

Gas-insulated switchgear (GIS) plays an important role as a modern power distribution device in power plants and power stations, which is commonly filled with SF6 insulating gas. During the equipment operation, the inevitable partial discharge causes SF6 to be broken down into gas (SF4, SOF2, SO2, and H2S), which degrades the insulation performance of the GIS. This paper is devoted to the detection of partial discharge and the removal of SF4 and SOF2, which are not conducive to insulation, by exploring new gas-sensing materials for characteristic gas detection. Based on first-principles calculation, on the one hand, the most stable adsorption configurations of rhodium-decorated gallium nitride nanotubes (Rh-GaNNTs) and gas adsorption systems were obtained. On the other hand, the doping and adsorption mechanisms were analyzed by band structure, density of states, deformation charge density, and molecular orbital theory. Subsequently, the gas-sensitive performance of Rh-GaNNTs for these four impurity gases was evaluated by analyzing the sensing response and recovery time. The adsorption stability and recovery time of Rh-GaNNTs to these gases are ranked as SF4 > SOF2 > SO2 > H2S; the order of influence of gas adsorption on sensitivity response is H2S > SO2 > SF4 ≈ SOF2. Calculation results show the potential of Rh-doped surfaces as reusable H2S and SO2 sensors and suggest their use as gas scavengers to remove SF4 and SOF2, especially SOF2.

Racing Demands for Winning a Grand Tour: Differences and Similarities Between a Female and a Male Winner.

To describe and compare the race characteristics, demands, and durability profile of a male and a female Grand Tour winner. Overall and stage-type-specific (ie,time trials, flat, semimountainous, and mountain) demands and race characteristics during 2 Grand Tours were determined and compared between the female and male cyclists. In addition, relative power output distribution and pacing, percentage of functional threshold power (FTP), and changes in maximal mean power outputs (MMPs) with increasing levels of kilojoules burned were determined. Although many differences were found between course and absolute racing demands between the male (FTP: 413W; critical power: 417W) and female (FTP: 297W; critical power: 297W) cyclists, similar power distributions and pacing strategies were found if data were expressed relatively. However, the female cyclist rode a higher percentage of her FTP during the first 2 quarters of flat stages (14.7%-15.1%) and the last quarter of mountain stages (9.8%) than the male cyclist. Decrements in MMPs were only observed after burning 30kJ·kg-1 in the female and 45kJ·kg-1 in the male Grand Tour winner. Both the male and female Grand Tour winners produced very high 20- to 60-minute MMPs, whereas decrements in MMPs were only observed after having burned 75% (female) and 80% (male) of total kilojoules burned during a stage. These are the latest and lowest in MMPs reported in the scientific literature and highlight the importance of durability in combination with excellent climbing and time-trial skills, which are needed to be able to win a Grand Tour.

Characterization of the Power Distribution Network for Commercialized STM32s Using a Resonance Frequency Measurement Method

Power integrity is a critical aspect of microcontroller (MCU) system design. The present tendency of increasing current density and operating frequency, along with decreasing operating voltage, significantly diminishes voltage margins. Given the cost efficiency required for MCU systems, this context places important constraints on the design of the power distribution network (PDN), which directly impacts power supply noise. Therefore, characterizing the PDN is necessary. This paper introduces a cost-effective measurement and modeling method to estimate the die-package resonance frequency of the PDN, a major threat to power integrity. The method, applied to two 32-bit MCUs from STMicroelectronics with varying PDN configurations, enables the identification of the die-package resonance frequency. The results lead to the refinement of the die capacitance model for both cases, with a maximum relative error of less than 7%. The final objective is to implement the measurement system in the die in order to adjust the PDN if necessary.

Optimizing Solar Power Generation and Integration in Automotive Systems Through IoT

Objective: This study presents an Internet of Things (IoT)- based system with edge intelligence that predicts power production with over 98% accuracy and monitors substations and smart solar installations, ensuring reliable power distribution in industrial IoT environments. This system enhances sustainability, safety, and energy management in smart buildings by reducing power fluctuations by 30% and improving decision-making, leading to a 95% reduction in energy management costs. Method: An IoT-enabled power monitoring system was implemented for smart solar panels and substations, incorporating edge intelligence for instantaneous prediction and decision-making. An IoT-enabled solar charging station was deployed for smart homes and Industry 4.0 applications. The cloud was used for analyzing sensor data, with a response time of less than 1 second. Findings: The proposed framework increased the efficiency and reliability of power production and distribution by 25% across commercial, residential, and industrial contexts. It significantly mitigated power fluctuations, reducing downtime by 40% and achieving a 95% cost reduction in energy management compared to traditional systems. IoT integration improved safety and sustainability metrics by 20% in smart buildings. Novelty: The framework integrates edge intelligence with IoT in smart solar systems and substations, providing a sophisticated control system that enhances power distribution decision-making. It facilitates real-time monitoring and prediction of power production with over 98% accuracy, emphasizing sustainability, safety, and energy management improvements of up to 30% in smart buildings. Keywords: IoT-based control system, Smart solar systems, Edge intelligence, Power substations, Load management, Energy sustainability

Whose Side Are We On? Toward an Emancipatory Ethic in Critical Arts-Based Research

The renowned U.S. sociologist Howard S. Becker, writing in 1969, posed the ethical question: Whose side are we on? In the context of continued structural inequalities in the distribution of power and wealth and individual and institutional oppression, in terms of class, race, gender, disability, and sexual orientation, this question remains as important as ever. The article, in advocating a robustly partisan approach, offers an emancipatory ethical framework continuum and an empirical example, from the authors’ critical arts-based research with undocumented Mexican Americans, to support and inform critical researchers, committed to working with oppressed groups against discrimination and for social justice.

Market Concentration of Branded Rice in Kerala

In Kerala rice is a staple diet. As per the information from Kalady Rice Millers’ Consortium, 40 per cent of the consumers in Kerala prefer branded rice, and out of this Kerala absorbs only 16 per cent, the rest of the branded rice is from outside states like Andhra Pradesh, Tamil Nadu and West Bengal. This study investigates the market concentration of Kerala’s branded rice market, focusing on the dominance of a few key players and its market share, pricing power, and concentration. The analysis employs four-firm concentration ratio, the Herfindahl-Hirschman Index (HHI), Gini coefficient and the Lorenz curve to reveal an oligopolistic structure, where a small number of firms control a substantial portion of the market. Major players like Pavizham, Keerthi Nirmal, Periyar, Kottaram, and Jayabharath, alongside the Kalady Rice Miller’s Consortium, play a crucial role in shaping the branded rice market in Kerala. The study involved a survey conducted between November 2023 and June 2024, with a random sample of 10 non-exporters, 5 exporters, and one co-operative rice mill, selected from the 46 branded rice manufacturers in the state, predominantly from Ernakulam and Palakkad districts. The findings of the study highlighted that the branded rice market in Kerala had an oligopolistic behaviour. This research fills a gap in understanding the distribution of market power in Kerala’s rice industry and offers suggestions for fostering greater competition and inclusivity in the market.

The Mindful Brain at Rest: Neural Oscillations and Aperiodic Activity in Experienced Meditators

Abstract Objectives Previous research has demonstrated that mindfulness meditation is associated with a variety of benefits, including improved mental health. Researchers have suggested these benefits may be underpinned by differences in neural oscillations. However, previous studies measuring neural oscillations have not controlled for non-oscillatory neural activity, the power spectrum of which follows a 1/f distribution (whereby the power of neural activity at each frequency is inversely proportional to that frequency (such that as frequency increases, power decreases)) and contributes to power measurements within oscillation frequencies of interest. We applied recently developed methods to determine if past findings related to neural oscillations in meditation are present even after controlling for non-oscillatory 1/f activity. Method Forty-eight experienced meditators and 44 demographically matched non-meditators provided resting electroencephalography (EEG) recordings. Whole-scalp EEG comparisons (topographical ANOVAs) were used to test for differences between meditators and non-meditators in the distribution or global power of activity for theta, alpha, beta, and gamma oscillations, and for the 1/f components slope and intercept using the extended Better OSCillation detection toolbox. Results Results indicated that meditators showed differences in theta, alpha, and gamma oscillatory power compared to non-meditators (all p < 0.05). Post hoc testing suggested that the oscillatory differences were primarily driven by differences in the distribution of neural activity between meditators and non-meditators, rather than differences in the overall power across all scalp electrodes. Conclusions Our results suggest that experience with meditation is associated with higher oscillatory power and altered distributions of theta, alpha, and gamma oscillations, even after controlling for non-oscillatory 1/f activity. Band-specific differences in oscillatory activity may be a mechanism through which meditation leads to neurophysiological benefits. Preregistration This study was not preregistered.

Service center in electricity distribution sector with variable peak electricity time: A two-stage queuing model approach with nested simulation

Customer service is essential for any business, significantly impacting customer satisfaction, brand reputation, and reducing associated costs. In the electricity industry, this importance is heightened due to the critical nature of electricity in daily life and growing usage rate of new technologies. Effective customer service in electric distribution companies involves handling a wide range of inquiries, from billing issues to technical support, requiring both remote and on-site assistance. The variability in customer calls, influenced by factors such as peak electricity hours, and different service and customer types, presents unique challenges in designing efficient service centers. This research develops a tailored model for the service center operations of a power distribution company, focusing on optimal staffing levels within budget constraints. A two-stage queuing model is proposed to address the needs of residential and non-residential customers. The study employs nested simulation techniques to account for variability in customer arrival times and peak hours. Key performance indicators (KPIs) are used to evaluate the effectiveness of staffing policies, considering both quantitative and qualitative costs. The study integrates stochastic optimization and ranking and selection methods to identify the best staffing configurations. The findings offer actionable insights for improving customer service efficiency and operational effectiveness in electricity customer service centers.

Spark: A Statistical Comparison and Evaluation of Classification Algorithms for Fault Prediction in Electrical Secondary Distribution Network

Managing faults in the electrical secondary distribution network is a challenging task given the nature, size, and complexity. Predicting faults early before they occur helps in increasing the safety and reliability of the power distribution system. Various statistical and machine learning techniques are being used to predict different types of faults. This study applies classification algorithms available in the big data framework Apache Spark through its python interface PySpark to predict electrical secondary distribution network faults. The study evaluates and compares nine algorithms: Decision tree, Gradient-boosted tree, Logistic regression, Naïve Bayes, Multilayer perceptron, Random forest, Linear Support Vector Machine, One-versus-rest and Factorization machines. The research uses Friedman’s test followed by the Nemenyi post hoc test to find the significance of performance differences among the algorithms. The results show significant differences among the algorithms. Gradient-boosted tree and One-versus-rest with Gradient-boosted tree had the best performance for binary and multiclass classification, respectively, while Naïve Bayes had the worst performance.

Investigations on opportunities and challenges of brilliant high-power laser beam welding with 24 kW and adjustable power distribution for different materials

Solid-state laser beam sources offer the possibility of generating high-brilliance laser beams with low expansion and high usable intensity at the focal point. New approaches include beam shaping with the use of core and ring fiber and, therefore, variable power distribution in the laser beam focal point and material interaction area. Particularly, high-power laser beam welding benefits from beam shaping because of the stabilizing effect on the weld pool. Furthermore, the technical progress achieved with regard to beam quality also allows one to achieve high Rayleigh lengths and, therefore, a more uniform beam diameter over the whole material thickness. In this study, investigations on high-power laser beam welding with a 24 kW disk laser beam source are conducted for three different materials (mild steel, aluminum alloy, and copper), which are of high interest for welding in different sectors. The influence of power distribution between the core and the ring as well as welding speed on weld geometry (depth and width), weld pool stability, and the resulting weld seam quality is investigated. It is shown that the welding process cannot just be scaled up in comparison with welding with lower laser beam power but has its own challenges. It is possible that high welding depths (12 mm for copper, more than 12 mm is possible for aluminum, and 25 mm for mild steel) could be achieved in one pass. To achieve this, aluminum needs the lowest energy per unit length per mm of sheet thickness and copper the highest.

Dynamic Adaptive Charging Network Planning Under Deep Uncertainties

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful.

The Design of a PV System of a Multi-Location Large Consumer: A Case Study from Al-Balqa Applied University

This research applies the suggested photovoltaic solar system design to Jordan’s Al-Balqa Applied University. Twelve of the twenty-four college buildings are located on the main campus, while the remaining twelve remote colleges are dispersed throughout Jordan’s many geographic locations, ranging from the far north to the far south. Azimuth angle, irradiation effect, building size, location, power consumption, and student population are all taken into account in the suggested design. Additionally, in-site generation and remote-wheeling generation are two types of solar system installation designs that are taken into consideration. A method for calculating the optimal location of remote-wheeling generation is proposed based on the centroid mass theory. The proposed method is applied in four scenarios. The first three scenarios are single-point, two-point, and three-point centroid locations. The fourth scenario is based on three single points, considering the power distribution regions in Jordan, which cover three regions. The results of the four scenarios will be discussed and demonstrated along with a comprehensive comparison between in-site generation, single-centroid-point remote-wheeling generation, and three-centroid-point remote-wheeling generation.

Global health resource distribution during COVID-19 and in future pandemics: Here’s what people say

Abstract During the COVID-19 pandemic, countless individuals across the world needed novel and sophisticated health care resources simultaneously, leading to dramatic global shortages. How to distribute resources in such situations? This dilemma is neither new nor understudied: Its complexity is well known to medical ethicists, public health scientists and philosophers. Building on this knowledge, powerful global distribution frameworks were proposed already early into the pandemic. Despite this knowledge, no globally consensual regulations existed (and exist) on the political sphere. Worse still, numerous countries hoarded resources for themselves, increasing global inequalities and prolonging the pandemic for everyone. For future emergencies, it is thus necessary to develop immediately applicable strategies. Because public acceptance is key for such interventions, evidence on the public opinion towards allocation principles is strongly needed. In 2021, we asked representative samples of N = 2694 adults in England and Germany to rate seven COVID-19-specific global allocation principles. In line with literature on justice attitudes in general, extensions of bifactor(S-1) models showed that participants would have preferred a more equity- or equality-based global resource distribution during the pandemic, presenting themselves as more cosmopolitan than global leaders. Trying to understand these attitudes better, we tested preregistered hypotheses on relations with other constructs and found positive associations between equity- and equality-directed preferences and global human identification, among others. To ensure generalisability on future pandemics, we collected data from a second cohort in spring 2024. All results will be presented in detail. With their responses, European citizens call upon the development of fairer and more efficient global distribution mechanisms. This global public health perspective could inform preparations for future global emergencies. Key messages • European citizens would have preferred a more equity- or equality-based global distribution of scarce health care resources during the COVID-19 pandemic than global leaders organised. • Citizens’ global perspective on public health and their strong emphasis on global fairness should inform preparations for future pandemics and other global emergencies.

The judicial response to rent controls in Europe: Protecting property rights against state’s intervention?

AbstractThis essay examines, from a legal and economic perspective, the judicial response to rent controls in the EU focusing on three courts that operate at the fundamental rights and constitutional level: the European Court of Human Rights (ECtHR), the German Federal Constitutional Court (BVerfGE), and the Italian Constitutional Court. Based on an analysis of a sample of judicial decisions rendered over time, a convergent trend emerges: these Courts have recognized and effectively protected the landlord’s property rights against rent controls that were disproportionate and could not ensure a reasonable return on investment. This trend is prominent in the jurisprudence of the ECtHR: the Strasbourg Court has contributed to reshaping the distribution of power between tenants and landlords, encouraging the transition of Eastern and Southern European Countries to the common European housing market. In both upholding and striking down rent control measures, judges generally take market value and the comparative reference price as the preferred benchmarks for fair rent price. A consumption-based and financial characterization of housing, coupled with the fundamental right to derive economic benefits as core elements of the right to property, underpin the legal reasoning of the three Courts.

Fivefold increased power handling in waveguide UTC-PDs using dual-injection shared CPW PD-arrays

This work presents two circuit-based solutions to enhance the power handling capabilities of waveguide-integrated uni-travelling carrier photodetectors (WG-UTC-PDs). Compared to a baseline WG-UTC-PD, these solutions achieve a fivefold increase in photocurrent before thermal breakdown. First, dual-injection improves the optical power distribution within a baseline WG-UTC-PD, raising the photocurrent threshold before thermal breakdown. Second, an array of four optically parallel WG-UTC-PDs, electrically connected to a single coplanar waveguide (CPW) line, further increases the maximum photocurrent by distributing the input optical power across multiple PDs. The design omits a termination resistor, as the arrays do not rely on a traveling wave configuration, maximizing photocurrent without a 50% reduction of bandwidth. Both 4-PD single-injection and dual-injection arrays were designed, fabricated, and characterized. Compared to a baseline UTC-PD, with a maximum photocurrent of 1.8 mA at a 3 dB bandwidth of 55 GHz, the 4-PD single-injection circuit achieved 5.1 mA at 43 GHz. The dual-injection array further increased the photocurrent to 9.2 mA at a bandwidth of 35 GHz. Electrical reflection measurements confirmed the negative effects of CPW losses on RF performance. These power handling improvements enable compact, high-power integrated solutions for microwave photonics.

Design and performance evaluation of a multi-load and multi-source DC-DC converter for efficient electric vehicle power systems

This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy source—specifically, solar energy—to enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100 W prototype demonstrated the converter’s ability to deliver stable 24 V and 48 V outputs from a 12 V input, with output voltage deviations kept within ± 1%, significantly improving upon the ± 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications.