Renewable Energy Systems

Enhancing photovoltaic models' performance and dependability requires optimal parameter extraction. This paper presents a practical method for determining these values from experimental current-voltage data: the war strategy optimization algorithm. RTC France, PWP201, and STP6-120/36 are the three PV models to which the war strategy optimization algorithm was successfully applied. According to the findings, the RMSE values for RTC France were 0.0000077298; PWP201 was 0.0020528; and STP6-120/36 was 0.0014253. These results demonstrate the great potential of the warfare strategy optimization (WSO) to improve the accuracy of photovoltaic models and advance photovoltaic technology.

for affording me an Assistantship without which I would not have had the chance to pursue my MS degree. I thank my committee chair, Dr. Ronald Falta and committee members Dr. James Castle and Dr. Lawrence Murdoch for their knowledge, guidance and support throughout this arduous graduate experience. I also thank Professor Alan Coulson, supervisor of the Teaching Assistants. I thank my parents, Leitha and Stephen Jarvis, whose sacrifices made it possible for me to obtain my primary and secondary school educations. I thank my brother Christian Giles for all the support he has given me through all of my tertiary level education programs. Lastly I thank my best friend, Noor, for being there whenever I needed laughter even as late or as early as 3 am.

for affording me an Assistantship without which I would not have had the chance to pursue my MS degree. I thank my committee chair, Dr. Ronald Falta and committee members Dr. James Castle and Dr. Lawrence Murdoch for their knowledge, guidance and support throughout this arduous graduate experience. I also thank Professor Alan Coulson, supervisor of the Teaching Assistants. I thank my parents, Leitha and Stephen Jarvis, whose sacrifices made it possible for me to obtain my primary and secondary school educations. I thank my brother Christian Giles for all the support he has given me through all of my tertiary level education programs. Lastly I thank my best friend, Noor, for being there whenever I needed laughter even as late or as early as 3 am.

This paper presents a comprehensive analysis of wind power integration in the IEEE 14-bus system, focusing on the impacts of variable wind generation on system performance. Using MATPOWER, we simulate the integration of up to three wind generators with realistic characteristics, including output variability and reactive power limitations. The study evaluates changes in system losses, voltage profiles, and power flows resulting from wind power integration. Our results demonstrate that wind integration can lead to significant changes in system behavior, including reduced overall losses under certain conditions and altered voltage profiles across the network. This work contributes to the growing body of knowledge on renewable energy integration and offers insights for power system planners and operators dealing with increasing wind power penetration.

The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.

This study presents a comprehensive exergy-based thermodynamic analysis of a standalone liquid air energy storage (LAES) system integrated with internal thermal storage and an Organic Rankine Cycle (ORC) for sustainable large-scale energy storage applications. Unlike conventional studies, this work focuses on providing a scalable design framework by quantifying storage fluid requirements on a per-unit-mass-flow and per-MWh-capacity basis, enabling the results to be generalized for various power outputs and storage capacities. The proposed system configurations with two-and three-stage compression were compared in terms of liquid yield, round-trip efficiency (RTE), exergy efficiency, and storage fluid requirements. Results indicate that the optimal operating pressures are 190 bar for charging and 130 bar for discharging. At 200 bar charging pressure, the liquid yield increases from 0.51 (at 60 bar) to 0.86, while the maximum RTE reaches 62% in the base case and 68% with ORC integration. Incorporating ORC enhances the RTE by approximately 6-7% compared with conventional configurations through improved low-grade waste heat recovery and energy utilization. The two-stage compression configuration with ORC demonstrates the best thermodynamic performance, providing higher exergy efficiency, greater net power output, and lower thermal storage requirements. Furthermore, the reduction in thermal storage fluid demand contributes to improved resource utilization and lower infrastructure requirements for large-scale deployment. Additional sensitivity analyses indicate that thermal losses significantly reduce system performance, whereas ambient temperature fluctuations within ±15 K have only a minor influence on round-trip efficiency and liquid yield due to compensating effects between charging and discharging processes. The findings of this study provide scalable design insights for LAES systems and demonstrate the potential of ORC-assisted LAES technology to support renewable energy integration, sustainable grid flexibility, and low-carbon energy infrastructure development.

Water supply remains a major developmental challenge in rural communities across sub-Saharan Africa, particularly in Nigeria where millions of households lack access to safe and reliable drinking water (World Health Organization & UNICEF, 2023; Sojobi et al., 2014). This research was conducted in Gombi Local Government Area of Adamawa State, Nigeria. Using a mixed-methods approach with 382 randomly selected respondents across five communities (Ga-anda, Garkida, Gombi South, Gombi North, and Fotta), data were collected through structured questionnaires, focus group discussions, and personal observations. Factor analysis and multiple linear regression were employed to identify and quantify factors affecting water supply. The results revealed four principal factors: water supply facilities (19.3%), climatic conditions (17.9%), groundwater availability/location (20.2%), and human activities including population growth (20.3%). The KMO measure (0.762) and Bartlett's test of sphericity (χ²=3190.94, p<0.001) confirmed the adequacy of the factor analysis. The regression model demonstrated strong predictive power (R²=0.788, F (4,377) =350.138, p<0.001), explaining approximately 79% of the variation in water supply. The study concludes that addressing water supply challenges requires integrated interventions targeting infrastructure development, climate adaptation strategies, groundwater exploration based on topographical considerations, and sustainable population and land use planning (Joshua et al., 2023; Federal Ministry of Water Resources, 2023).

A pedal operated exercise machine for generating electricity was designed and constructed. The aerobics machine generator system includes a generator adapted for coupling to an exercise cycle. The generator produces electricity when the exercise cycle rotates a drive wheel of the generator via a belt. The exercise cycle was designed based on anthropometric data fitting 95% of the students of the College of Engineering, Igbinedion University, Okada. As the operator exercises, the rear wheel of the cycle turns a generator via a belt. The elecetriciy generated is stored in a 12V battery which is in turn connected to an inverter that converts power from 12VDC to 110 AC so that it can be used with everyday appliances such as phones, laptops, etc. Testing showed that a power ouput of 42 Watts was generated by the user number one, one of the ten userc involved in the test.
Keyword: Aerobic machine, anthropometric data, design, generator, everyday appliance

Digital transformation has become a strategic imperative for organisations seeking resilience in volatile emerging markets. However, the mechanisms through which digital transformation enhances organisational resilience remain underexplored, particularly the roles of employee agility and sustainable leadership. This study investigates the mediating role of employee agility and the moderating role of institutional support in the relationship between sustainable leadership, digital transformation, and organisational resilience. Drawing on dynamic capabilities theory, social exchange theory, and institutional theory, we propose a moderated serial mediation model. Using a time-lagged, multi-source survey design, we collected data from 562 employees and 112 managers across 124 manufacturing and service firms in Nigeria, Ghana, and Kenya. Structural equation modelling (SEM) and hierarchical linear modelling (HLM) were employed to test the hypotheses. Results indicate that sustainable leadership positively influences digital transformation (β = 0.48, p < 0.001) and organisational resilience (β = 0.39, p < 0.001). Digital transformation partially mediates the relationship between sustainable leadership and resilience (indirect effect = 0.16, 95% CI [0.10, 0.23]). Employee agility mediates the digital transformation-resilience link (indirect effect = 0.21, 95% CI [0.14, 0.29]). Institutional support significantly moderates the relationship between digital transformation and employee agility (β = 0.24, p = 0.001), and the moderated mediation index is significant (index = 0.08, SE = 0.02). The findings suggest that sustainable leadership enables digital transformation, which in turn enhances employee agility, but this pathway is strengthened by institutional support. Theoretical contributions, practical implications for managers and policy makers, and future research directions are discussed.

Floating photovoltaics (FPV), or floatovoltaics, offers several synergistic benefits over land-based PV, but has not been investigated for cold climates. This study introduces a 7 kW semi-flexible monocrystalline foam-backed FPV system operating on a pond in the cold climate of Ontario Canada and equipped with an air bubbler system for ice prevention. The system is monitored using an open source data acquisition platform that records meteorological variables, water and module temperatures, electrical output and high resolution imagery in both summer and winter conditions. A comparison of measured FPV temperatures with those simulated by previous models revealed a notable discrepancy during the winter season. A regression model developed in this study indicated that the foam-based FPV system generated 7.7. MWh/year, representing up to 2.7% more energy than other PV models (e.g. Faiman FPV, Kamuyu et al., etc.). The FPV array storm water pond coverage scaled linearly with evaporation reduction, reaching a maximum of 927 m3/year if 50% of the pond is covered, demonstrating the potential for foam-based FPV to save water and support agricultural water needs. In addition, the deployed air-bubbler system successfully maintained ice-free open water throughout the winter season with negligible additional energy consumption, ranging from 0.02% (1.9 kWh) to 14.5% (893 kWh) of the total annual yield. The new methodological approach, detailed in this study, enables FPV operations of any kind in cold climates where ice formation has historically constrained deployment. Finally, the system achieved a positive net present value (∼57,000 $CAD) under a high electricity price scenario ($0.55 CAD/kWh) for off-grid systems, yielding a discounted payback period of 4.2 years. Overall, the results of this study established foam-based FPV as a promising and adaptable platform for renewable energy generation, introduces a transferable ice-melting model for cold-climate operations, and highlights the unique performance dynamics of flat-tilt FPV modules in icy environments. These advances provide a solid foundation for future research at larger scales and across diverse water bodies, thereby positioning FPV as a viable technology for sustainable energy expansion not only in warm climates but also in cold regions.

Surface defects in solar panels have a detrimental impact on the performance and reliability of the system. In this research paper, we proposed an automatic fault detection model for solar panels using a deep learning model based on the VGG16 (CNN) architecture with transfer learning and data augmentation. A dataset that has a total of 869 images for six fault categories (Clean, Dusty, Bird-drop, Electrical-damage, Physical-damage, Snow) was used. Two models were utilized for analysis: (Model 1) the baseline VGG16 which is the VGG16 architecture pre-trained on the original dataset scope; and (Model 2) the enhanced VGG16 which incorporates data augmentation, freezing of early layers and dropout regularizing. Model 2 achieved a higher accuracy at 98.76% on validation set compared to Model 1 with an accuracy of 82.49% indicating that comprising transfer learning along and pertinent augmentation positively enhances the classification accuracy. The proposed approach is capable of being scaled up and replicated in real-world applications of smart automated solar panel inspection.

Now days, ingesting of power goes outsidedeprived of boundary due to creation and novelty of other products that stresses the power. So, recently countries together formed "International Solar Alliance" (ISA) which targets to obtain all energy from sun because, they stated that the 'Sun is the source for all energy'. As a result, the findings of this study have established the significance of solar energy in empowering the public's economic components through effective recommendations and research reflections. The study with a total of 300 sample dependants and consequences unprotected on determinations, savings and satisfaction among solar panels fitted clients.

This paper investigates strategies to address climate change by promoting sustainable energy technologies and consumption practices. It examines renewable energy sources such as solar, wind, and geothermal and their potential to reduce greenhouse gas emissions. The study also explores energy optimization techniques, focusing on genetic algorithms (GAs) and smart energy systems like smart grids and microgrids, which enhance energy efficiency and sustainability. The research highlights the role of the circular economy in fostering sustainable consumption through recycling and waste management. Furthermore, the paper explores the economic trade-offs between energy consumption and environmental harm, focusing on the impact of both renewable and fossil fuel energies. A dual methodological approach is employed: first, an endogenous growth model excluding environmental factors, followed by a modified version incorporating environmental considerations. Using a continuous genetic algorithm and data from 2000 to 2025, the study forecasts the optimal trajectory for renewable energy's share in global energy consumption under two scenarios. The findings suggest that by 2025, renewable energy could represent 82.4% of the total energy consumption under environmental constraints, up from the current share of 45%. This growth is hindered by challenges like droughts, which impact hydropower production. The study concludes that achieving a sustainable energy transition requires comprehensive policies integrating renewable energy expansion, energy efficiency, and environmental protection. These findings provide important insights into optimizing energy pathways for economic growth and environmental sustainability. They also serve as a foundation for future research and policy recommendations, aiming to ensure a low emission future by balancing the need for energy consumption with the preservation of the environment.

Achieving universal electricity access in rural areas remains a complex challenge in many developing countries, particularly for communities located within reach of existing distribution infrastructure but not yet connected. In such contexts, decision-makers must often choose between extending the main grid and deploying off-grid systems. This study presents a structured scoping review based on bibliographic sources, aimed at identifying how decision-making processes are supported in selection of rural electrificationstrategies, when both options are technically and economically viable. Following the PRISMA-ScR guidelines, a multi-phase filtering strategy was applied to the Scopus database, covering literature published between 2013 and 2024. A total of 3780 documents were initially retrieved, from which 136 were selected for in-depth analysis. Data extraction, co-citation mapping, keyword clustering, and thematic coding were used to classify the literature into five decision-related domains: technology selection, network configuration, system optimisation, policy frameworks, and multi-criteria methodologies. The review identifies recurring methodological patterns and systematises the decision-making criteria most frequently applied in rural electrification planning. It highlights that current approaches often treat grid extension and offgrid alternatives within isolated frameworks, despite their coexistence in practical planning scenarios. The analysis reveals significant gaps in the integration of technical, economic, social, environmental and institutional dimensions, as well as in the use of unified indicators that enable meaningful comparisons. These findings emphasise the need for more comprehensive frameworks that reflect the complexity of electrification choices in grid-adjacent rural areas and support more consistent, evidence-based planning processes.

The global transition toward sustainable energy paradigms necessitates sophisticated multi-criteria decision-making (MCDM) models capable of handling epistemic uncertainty, linguistic vagueness, and complex criteria interdependencies simultaneously. This paper presents a mathematically rigorous and comprehensive evaluation framework that integrates Quantum Spherical Fuzzy Sets (QSFS) with the Golden Cut ratio, Shannon’s Entropy, and the Weighted Aggregated Sum Product Assessment (WASPAS) methodology. Unlike conventional fuzzy sets, QSFS utilizes amplitude and phase angles to decouple membership dimensions under quantum superposition properties, while the Golden Cut bounds the linguistic transition limits. We identify and resolve several structural and numerical inconsistencies prevalent in early fuzzy energy evaluation literature, including negative entropy bounds, division-by-zero singularities in score functions, and index boundary mismatches during vector normalization. The expanded framework is deployed to evaluate the Renewable Energy Investment (REI) competencies of G7 economies across twelve distinct dimensions derived from recent literature. Empirical results indicate that customer-centric expectations and real-time financial tracking are the most decisive core competencies. Comprehensive sensitivity analysis and Monte Carlo simulations confirm that Italy exhibits the highest structural resilience and investment competency, followed systematically by the United States and France.

O chamado "Triângulo do Lítio", formado por Bolívia, Argentina e Chile, concentra algumas das maiores reservas conhecidas de lítio do planeta e tornou-se uma das regiões estratégicas centrais da transição energética global. Este artigo propõe uma análise investigativa crítica vetorialética sobre as implicações geopolíticas, ecológicas e civilizacionais da expansão da mineração de lítio na América do Sul. Argumenta-se que o lítio não constitui apenas um recurso mineral, mas um operador vetorial de reorganização territorial, econômica e tecnológica planetária. O trabalho investiga as tensões entre transição energética, extrativismo verde, escassez hídrica, financeirização mineral, dependência tecnológica e reconfiguração geopolítica global. Defende-se que a chamada "economia verde" reproduz, em nova frequência, antigos padrões coloniais de exploração periférica, transformando salares andinos em zonas de pressão geoeconômica extrema. Sob perspectiva vetorialética, o lítio emerge como condensador contemporâneo de tensões entre persistência ecológica planetária e aceleração metabólica da civilização industrial.

In this paper, the field tests conducted by the IITREE-LAT to verify the performance of motor-generators are presented. These machines are used as distributed energy resources (DER) in oil fields and different cities across Argentina. On the basis of these tests, the models regarding generator, power-speed control system and voltage control system are presented.

Con el objeto de caracterizar el funcionamiento de dicho Parque se realizaron mediciones de campo. Por un lado, se cuenta con mediciones de potencia activa y de velocidad de viento en cada uno de los 21 aerogeneradores. Por otro, se realizaron mediciones normalizadas de armónicas en la tensión y la corriente (conforme a lo establecido en la Norma IEC 61000-4-7) [1] y de flicker (según lo estipulado en la Norma IEC 61000-4-15) [2] en barras internas de 31,5 kV durante un período de tres semanas. En el artículo se presentan y analizan detalladamente los resultados de tales mediciones. En lo que se refiere a mediciones de viento y potencia en los distintos aerogeneradores individuales, se comparan las curvas Velocidad del Viento versus Potencia medidas con las provistas por el fabricante. En cuanto a las mediciones de armónicas y de flicker se presentan los niveles registrados de tales perturbaciones y se evalúan según el estado del Parque, en términos de potencia despachada. Finalmente, a partir de los resultados de las mediciones, se concluye acerca del desempeño global del Parque, haciendo especial hincapié en los niveles de perturbaciones emitidas por el mismo.

The Multi-Stage Flash (MSF) desalination technique continues to be a prevalent freshwater production method especially in desert areas. Its energy demands create major economic and environmental obstacles. The investigation examines cutting-edge methods to boost MSF energy performance through the integration of advanced heat

PARTE 1: INTRODUCCIÓN                                                                  9
1. COMPROMISO INSTITUCIONAL                                                  10
2. JUSTIFICACIÓN Y ANTECEDENTES                                            13
3. OBJETIVOS, ENFOQUE Y PRINCIPIOS RECTORES                    20
PARTE 2: CONTEXTUALIZACIÓN                                                      25
4. MARCO CONCEPTUAL                                                                  26
5. CONTEXTO NORMATIVO E INSTITUCIONAL                            30
6. DIAGNÓSTICO DE SITUACIÓN                                                    41
A. LGTBIFOBIA, DISCRIMINACIÓN Y DELITOS DE ODIO              43
B. LABORAL                                                                                        52
C. SALUD                                                                                            60
D. EDUCACIÓN                                                                                  67
E. CULTURA, OCIO Y DEPORTE                                                      74
F. MEDIOS DE COMUNICACIÓN E INTERNET                              80
G. MEMORIA LGTBI                                                                          83
H. ACCIÓN EXTERIOR Y PROTECCIÓN INTERNACIONAL          86
PARTE 3: CONTENIDO DE LA
ESTRATEGIA                                                                                      89
7. PROCESO PARTICIPATIVO Y METODOLOGÍA                          90
8. MEDIDAS                                                                                        92
9. GESTIÓN DE LA ESTRATEGIA                                                  157
I.VIGENCIA Y ANUALIZACIÓN                                                      157
II. SEGUIMIENTO Y EVALUACIÓN DE LA ESTRATEGIA            158
ANEXOS                                                                                            163
BIBLIOGRAFÍA                                                                                  164
LISTADO DE ACRÓNIMOS                                                            175

The intermittency of electricity supply from conventional sources, increase in fuel prices and constant emission of greenhouse gases by non-renewable energy sources are major challenges faced by energy users. Energy from reneweable sources have advantages over the traditional (non-renewable) sources of energy. This paper presents the sizing and analysis of a standalone photovoltaic (PV) system for a 3-bedroom residence situated at Obollo Nsukka (6.876°N, 7.403°E, 389 m) in Nigeria. The energy requirements of such a residence are 8.14 kWh/day and analysis have shown that the cost of constructing the PV system is ₦2,838,040 Nigeria naira (NGN). The cost of maintaining such a system within a lifetime of 20 years is between 159,328 NGN/year to 1,895,918 NGN/year. Comparing the levelized cost of energy (LCOE) of enugu electricity distribution company (EEDC) which is 66.5 NGN/kWh to the LCOE of the standalone PV system which is between 102,124 NGN/kWh to 419 NGN/kWh it was found out that the cost of electricity from the PV system is more than that of the conventional grid. The PV system provides feasible solution to the intermittency issues of the conventional grid in Nigeria. Hence, this technology only technically viable for residential electrification purposes in Nigeria.

The growing trend toward electric vehicle adoption in urban situations is a constant, but the same cannot be said for charging infrastructure that has not been able to keep up with its traditionally volatile nature and relationship to environmental conditions. Current systems are predominantly based on inelastic schedules or single-objective optimization, resulting into peak load stress, wasted energy and unintended rise to localized pollution. The mentioned gap is addressed in this work by proposing a real-time adaptable Multiple Decision Making-based IoT framework for Electric Vehicles charging decisions, considering both grid conditions and air quality signals with Reinforcement Learning. It incorporates charging station data, traffic flow and pollution sensors into the system wrap a model of environment to be as a decision process with a continuous adjustment of charging action by PPO-based learning on that. The proposed strategy is to trade-off between energy-efficient and emission control by jointly optimizing them instead of separately optimizing. The system smooths the demand for charging by learning from temporal patterns and feedback from its surroundings, thus avoiding high-impact zones when pollution spikes. Experimental evaluation demonstrates strong performance-offering a 97.2% accuracy with low prediction errors (RMSE: 0.018, MAE: 0.012). It also decreases makespan by 28%, increases energy efficiency by 31% and decrease operational cost by 26%. It is also relevant to the environment as it lowers pollution by 22%. The proposed system is more stable because it adopts a clustering, supervised learning strategy compared to traditional rule-based, linear programming and even regular deep learning models; faster decision-making in terms of this procedure; less overfitting or adaptiveness which results in consistent multi-objective optimization. Such results suggest that adaptive, learning-based control may open an achievable pathway to smarter, cleaner urban charging systems.

As our society ages, the prevalence of age-related frailties increases. There is a pressing need for tailored solutions to address these ailments effectively. The demand for tissue engineering is steadily rising, with a particular emphasis on utilizing stem cells to meet this need. Treatments incorporating ultrafast laser-matter interaction processes are becoming increasingly prominent, thanks to their numerous appealing attributes. These include Nano-scale spatial resolution, no thermal characteristics, and the ability to facilitate non-destructive engineering procedures. This review discusses innovative laser-based methods for printing materials suitable for biological applications and for modifying surfaces at the micro-and nanoscale. Simultaneous methods involve solving a series of measurements concurrently, employing various modes, discrete or continuous. On the other hand, information transmission techniques focus on modeling unique scales and injecting reflections at a continuous level within messaging programs Investigating the utilization of Multi-Attribute Decision Making (MADM) methods in facilitating vertical handovers within wireless networks, this study delves into the integration of weighing with Gray Rational Analysis (GRA). It evaluates the effects of these techniques and ultimately aims to enhance the precision of fault diagnosis across the entirety of the power grid. Specifically, it reanalyzes faults within overlapping regions utilizing the GRA methodology. The viability and effectiveness of the approach are assessed through examination of two instances. Concurrently, a combination of gray communication analysis and analytic hierarchy process is employed to construct an assessment framework. This framework adeptly manages uncertainties in water infiltration impact indices, providing a literal representation of the significance of each effect. Based on the analysis of technological advancements within software companies, a study was conducted to identify key traits. Subsequently, an assessment metric for innovation proficiency was devised, focusing on the capacity to introduce and develop novel technologies. This evaluation utilizes both Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA) algorithms for estimation purposes. Unique Properties is got the first rank whereas is the Challenges and Opportunities is having the Lowest rank.

The work portrayed in this paper furnishes knowledge about advanced solar photovoltaic (PV) conversion system. This PV system is fast and tracks more output power than conventional PV systems. In literature, the PV parameters of a double exponential model were designed with the help of Levenberg-Marquardt algorithm. In this paper, the PV parametric values were taken into consideration and implemented in a single exponential model with curve fitting method. The results of a conventional PV system and it was observed are compared with the implemented PV system and observed that, the implemented PV system tracks 23% excess power than the conventional one. Finally, the PV system is implemented with a series of 2000 interconnected PV cells for higher voltage applications, due to its unfussiness and lower cost. The MATLAB simulated models are presented and discussed.

Chile stands out for its renewable energy resources and its commitment to developing green hydrogen. However, achieving cost parity with gray hydrogen remains an obstacle, mainly due to high capital costs and sensitivity to scale. This study assesses the technical and economic feasibility of green hydrogen production, using five different plants located in the Magallanes region in the south of the country as a reference. The model integrates a detailed framework of wind generation, PEM electrolysis, compression, and high-pressure storage subsystems, as well as a stochastic economic layer that combines the CAPEX, NPV, and LCOH assessments using Monte Carlo simulations. It also incorporates real-world capacity distributions and probabilistic fluctuations in systems. A sensitivity analysis confirms production scale as the main factor affecting profitability, with a break-even threshold of 0.5 MW. The results show that the LCOH decreases from 7.1 USD to 3.4 USD/kgH 2 as capacity increases. The analysis reveals that only 23.88% of small-scale configurations yield positive NPV, underscoring the need for scaling to achieve economic viability.

Fire detection systems have traditionally relied on single-parameter sensing mechanisms such as smoke or heat detection, which often result in delayed response and high false alarm rates. Recent advancements in Internet of Things (IoT) and computer vision have enabled the development of intelligent fire monitoring systems capable of real-time analysis and multi-parameter evaluation. In this study, an integrated fire detection system is proposed that combines multisensor environmental monitoring with image-based fire detection and IoT communication. The system utilizes an ESP32 microcontroller connected with MQ-2 smoke and DHT11 temperature sensors, along with an ESP32-CAM module for visual monitoring. Data is transmitted through a wireless network and processed using Python-based algorithms. An intelligent decision-support framework correlates real-time sensor measurements with visual feature extraction outputs to confirm fire conditions reliably and reduce false alarms. The experimental results demonstrate that the proposed system achieves reliable fire detection with low latency and improved accuracy compared to traditional systems. The system also supports real-time monitoring and alert generation, making it suitable for practical deployment in residential and industrial environments.

This study investigates the integration of artificial intelligence (AI) and machine learning (ML) technologies in solarbiomass hybrid energy systems for sustainable energy generation. Through a comprehensive empirical analysis of 250 hybrid energy installations across five regions, this research evaluates the performance optimization potential of AI/ML algorithms in renewable energy management. The study employed a mixed-methods approach with quantitative data collection from operational systems over 24 months (2023)(2024). Results demonstrate that AI-enhanced solar-biomass systems achieved 34.7% higher energy efficiency compared to conventional systems, with ML-based predictive maintenance reducing operational costs by 28.3%. The study found significant correlations between AI implementation and system reliability (r=0.847, p<0.001), while deep learning models improved energy forecasting accuracy to 92.4%. Integration challenges were identified in 18.2% of installations, primarily related to data synchronization and algorithm compatibility. These findings contribute to advancing sustainable energy technologies through intelligent optimization frameworks, providing empirical evidence for the transformative potential of AI/ML in hybrid renewable energy systems.

The transition toward sustainable energy systems in developing economies faces multifaceted constraints including limited financial resources, institutional capacity gaps, and policy implementation challenges. Conventional forecasting approaches for renewable electricity penetration predominantly emphasize technical and economic variables while neglecting the catalytic role of policy frameworks as dynamic predictors. This research introduces a novel machine learning architecture that explicitly integrates quantitative policy indicators derived from the World Bank's Regulatory Indicators for Sustainable Energy (RISE), IRENA's Renewables Readiness Assessments (RRA), and IEA Country Energy Profiles into a Long

(IEA) studies on concentrated solar power and long-duration energy storage. 2. Research literature on molten salt thermal storage systems and Power-to-Heat integration. 3. Renewable dispatchability studies involving hybrid PV-CSP infrastructure and grid balancing. 4. Engineering analyses on thermal storage optimization, steam-cycle efficiency, and solar hybridization systems.

Title: Low-Entropy Silicon Agents in UNITAS Energy Cycles: Theoretical Foundation and Computational Modeling.Subject of Research: This paper addresses the fundamental problem of low operational viability in supercritical power cycles based on carbon dioxide (sCO2). The author postulates that the current engineering crisis in this field is caused by ignoring the informational limits of energy density, as described within the Doctrine of Programmable Reality (UNITAS).Methodology: The research is based on a multidisciplinary synthesis of classical thermodynamics and computational ontology. The Basel Wall (B = 1.6449), which determines the energy packing limit, and the Reality Luft (L = 0.0269), which defines the zone of minimum dissipation (the G-slip effect), are used as key navigational markers. Comparative physical process modeling was performed using Python for various working fluids.Results:It is proven that sCO2 systems at pressures above 20 MPa exceed the threshold of informational stability, leading to inevitable material degradation and high parasitic loads.The advantage of using Hexamethyldisiloxane (Siloxane MM) as a working fluid is justified. It is established that the low critical pressure of MM (1.93 MPa) combined with its high molar mass (162) ensures system operation in an "informational resonance" mode with near-zero erosion.Calculations show that the real (Net) efficiency of siloxane units is 15-25% higher than theoretical sCO2 models due to a drastic reduction in compression and maintenance costs.Conclusion: The transition to "Silicon Energy" based on Siloxane MM allows for the creation of autonomous power modules with a maintenance-free cycle exceeding 25 years. The proposed engineering solution minimizes "operational friction" and represents an economically optimal response to the challenges of modern distributed power generation.

The need for energy sources that are both efficient and cheap, while simultaneously being environmentally
sustainable, emphasizes the shift from one form of energy to an alternative source. This review has examined
the transition from charcoal to briquette production in India. It uses various published works from reputable
sources to deduce information on the impact of charcoal production, transition to biomass, briquette
technologies, briquette feedstock, production, and its environmental and economic benefits. Challenges in
the transition, government policies, and intervention in briquette production are also reviewed. The review
revealed the devastating effects charcoal production has on the environment which exacerbates forest
depletion, air pollution, soil erosion, species extinction, and climate change. At the same time, briquette
production influenced rural livelihood and provided more efficient and cleaner energy due to its calorific
value, low emission, and reduced pollution. The biomass feedstock comprises various materials such as
rice husks, groundnut shells, sugarcane bagasse, corn husks, sawdust, cow manure, pine timber, bamboo
particles, plastic refuse, and forestry byproducts. The review investigates the myriad technological
methodologies employed in the production of briquettes. Various methods are used in briquette production
such as compressing the biomass into multiple shapes and sizes. It explores challenges such as cost in
initial setup, lack of awareness, seasonal variation in biomass availability, and lack of storage and supply
chain. The study recommends that further research and policy interventions be made to enhance the adoption
of briquette, provide efficient and affordable technology to rural communities, and expand markets for
briquette industries.

Canada’s energy transition is underway, but without a deliberate strategy to align clean energy investment with regional economic development, the benefits will remain unevenly distributed. Regional energy economy planning offers a framework for turning clean energy investment into a driver of jobs, industrial diversification, and community prosperity, rather than treating the energy transition and economic development as separate agendas.

Regional Energy-Economy Planning: A Framework to Accelerate Decarbonization and Support Economic Development draws on international experience to show how energy system transformation can be explicitly tied to regional economic strategies, industrial clustering, and local benefit-sharing.

For Canadian governments, the opportunity is clear: clean energy investment is about more than meeting climate targets. With the right planning framework, it can become a deliberate strategy for regional growth and community well-being.

Ce mémoire est le résultat d'un effort constant qui n'aurait pu aboutir sans la contribution de l'ING. RIMOUDAL DAKO ANTOINE MULER et Dr MADOMGUIA DIANE. Je tiens ici à exprimer mes profonds remerciements pour leurs conseils, leurs disponibilités et leurs rigueurs scientifiques. J'adresse mes sincères remerciements : Pr MOHAMADOU ALIDOU, Directeur de l'Ecole Nationale Supérieure Polytechnique de Maroua (ENSPM) qui n'a ménagé aucun effort pour la réussite de ma formation. Dr MOHAMADOU SALI, Chef de Département d'Hydraulique et Maîtrise des Eaux de l'Ecole Nationale Supérieure Polytechnique de Maroua pour nous avoir donné une formation de qualité ; pour ses grandes qualités humaines et ses conseils.

Ce mémoire est le résultat d'un effort constant qui n'aurait pu aboutir sans la contribution de l'ING. RIMOUDAL DAKO ANTOINE MULER et Dr MADOMGUIA DIANE. Je tiens ici à exprimer mes profonds remerciements pour leurs conseils, leurs disponibilités et leurs rigueurs scientifiques. J'adresse mes sincères remerciements : Pr MOHAMADOU ALIDOU, Directeur de l'Ecole Nationale Supérieure Polytechnique de Maroua (ENSPM) qui n'a ménagé aucun effort pour la réussite de ma formation. Dr MOHAMADOU SALI, Chef de Département d'Hydraulique et Maîtrise des Eaux de l'Ecole Nationale Supérieure Polytechnique de Maroua pour nous avoir donné une formation de qualité ; pour ses grandes qualités humaines et ses conseils.

Meeting India's climate commitments and securing long-term resilience towards a low-carbon economy demand a decisive transition to renewable energy. The country's thrust towards renewable energy is spearheaded by institutional machineries such as the Indian Renewable Energy Development Agency Limited (IREDA), policy frameworks like the National Solar Mission and initiatives such as the PM-KUSUM Scheme and several significant state level programs. This study investigates the contribution of IREDA through mobilization of green capital towards the development of renewable energy capacity, with particular emphasis on the solar energy sector. The study adopts a quantitative research approach that integrates descriptive and inferential methods, drawing on secondary data, to explore the relationship between solar financing initiatives of IREDA and the development of installed solar energy capacity of the country over the past decade. The analysis is structured in the context of India's global commitments on environmental sustainability, while also assessing whether the growth of the solar sector has significantly influenced the nation's overall renewable energy capacity. The findings underscore the pivotal role of IREDA in driving renewable energy development through green financing, highlighting its consistent prioritization of solar energy within its sectoral loan portfolio, thereby reflecting its strategic importance in India's clean energy transition.

Globalisasi telah mengubah industri konstruksi menjadi pasar yang semakin terbuka dan kompetitif melalui arus modal, teknologi, standar kerja, dan tenaga ahli lintas negara. Kondisi ini menghadirkan tantangan ganda bagi Indonesia: mempertahankan kepentingan nasional sekaligus meningkatkan kualitas sumber daya manusia agar mampu bersaing secara internasional. Artikel ini menganalisis hubungan antara nasionalisme dan profesionalisme dalam industri konstruksi serta berargumen bahwa keduanya bukan konsep yang bertentangan, melainkan saling menguatkan. Melalui pendekatan kajian literatur, artikel ini menegaskan bahwa nasionalisme kontemporer tidak lagi berarti proteksionisme sempit, melainkan komitmen untuk memperkuat daya saing bangsa melalui kualitas karya, etika profesi, inovasi teknologi, dan tata kelola proyek yang akuntabel. Profesionalisme dalam industri konstruksi tercermin pada penguasaan kompetensi teknis, kepatuhan terhadap standar keselamatan dan mutu, sertifikasi, integritas, serta kemampuan beradaptasi terhadap transformasi digital seperti Building Information Modeling (BIM) dan Konstruksi 4.0. Dengan demikian, tenaga konstruksi Indonesia tidak cukup hanya berbekal semangat kebangsaan, tetapi harus membuktikan kapasitasnya melalui kinerja yang unggul. Sinergi antara nasionalisme dan profesionalisme menjadi fondasi strategis dalam menjaga kedaulatan industri konstruksi nasional di tengah persaingan global.

Indonesia, with its tropical climate, possesses substantial solar energy potential. However, traditional monitoring of solar power systems in Indonesia still relies on manual observation, making the process inefficient, time-consuming, and prone to error. To address these limitations, this study proposes the design and implementation of a real-time Internet of Things (IoT)-based monitoring system for solar power plants using the Telegram application as the user interface. The system integrates the ESP32 microcontroller and the Pzem-004 T sensor to measure AC electrical parameters, including voltage, current, power, energy, frequency, and power factor. Unlike previous studies that used platforms such as Blynk or ThingSpeak, this research introduces Telegram as an innovative messagingbased monitoring platform, offering greater accessibility, simplicity, and user familiarity. The monitoring system was tested on a single-phase off-grid solar power setup, utilizing five types of household electrical loads, to validate its accuracy and reliability. The ESP32 communicates with the Telegram bot through Wi-Fi, and users can retrieve real-time data via predefined commands. Experimental results demonstrate high measurement accuracy, with average errors of 0.07% for voltage, 0.1% for current, and 0.08% for power. These results confirm that the system provides reliable data transmission and sensor readings. This work contributes a low-cost, efficient, and user-friendly alternative to conventional monitoring systems, particularly for decentralized renewable energy systems in remote or off-grid areas. The integration of Telegram as a communication medium for energy monitoring adds a novel dimension to IoT-based power system applications.

Indonesia, with its tropical climate, possesses substantial solar energy potential. However, traditional monitoring of solar power systems in Indonesia still relies on manual observation, making the process inefficient, time-consuming, and prone to error. To address these limitations, this study proposes the design and implementation of a real-time Internet of Things (IoT)-based monitoring system for solar power plants using the Telegram application as the user interface. The system integrates the ESP32 microcontroller and the Pzem-004 T sensor to measure AC electrical parameters, including voltage, current, power, energy, frequency, and power factor. Unlike previous studies that used platforms such as Blynk or ThingSpeak, this research introduces Telegram as an innovative messagingbased monitoring platform, offering greater accessibility, simplicity, and user familiarity. The monitoring system was tested on a single-phase off-grid solar power setup, utilizing five types of household electrical loads, to validate its accuracy and reliability. The ESP32 communicates with the Telegram bot through Wi-Fi, and users can retrieve real-time data via predefined commands. Experimental results demonstrate high measurement accuracy, with average errors of 0.07% for voltage, 0.1% for current, and 0.08% for power. These results confirm that the system provides reliable data transmission and sensor readings. This work contributes a low-cost, efficient, and user-friendly alternative to conventional monitoring systems, particularly for decentralized renewable energy systems in remote or off-grid areas. The integration of Telegram as a communication medium for energy monitoring adds a novel dimension to IoT-based power system applications.

robustness and accuracy improvements of Support Vector M-Quantile
Regression in nonlinear regression. This paper proposes a novel regression
framework, Support Vector M-Quantile Regression (SVMQR), which integrates
the robustness of M-quantile regression with the nonlinear modeling
power of Support Vector Machines. Unlike traditional Support Vector
Quantile Regression (SVQR), which relies on the asymmetric pinball loss,
SVMQR leverages the Huber loss function to enhance resistance to outliers
and improve estimation accuracy in heavy-tailed and contaminated settings.
The dual formulation of the model is derived, and an efficient algorithm
is implemented. Through extensive simulation studies under various
error distributions and a real-world application to the Medical Cost
Personal Dataset, SVMQR consistently outperforms SVQR and SVR across
multiple metrics, including Pinball Loss, MAE, and RMSE. These results confirm
the effectiveness of SVMQR in providing robust and accurate quantile
estimates, particularly in challenging data environments characterized by
heteroscedasticity and non-normality.

Utility-scale solar photovoltaics and onshore wind are central to power-system decarbonization, yet land availability, cumulative environmental impacts, and community acceptance increasingly constrain project pipelines. These constraints are often amplified by inconsistent land-use reporting: a project's direct impact area (land physically disturbed or occupied) differs from its total project area (the broader boundary associated with spacing, buffers, and parcels), leading to mismatched interpretations of trade-offs. This paper synthesizes peer‑reviewed evidence and agency guidance to develop a transferable framework for land‑use optimization in renewable energy siting. The framework integrates (i) geospatial screening and suitability mapping (GIS‑based multi‑criteria decision analysis), (ii) multi‑objective portfolio optimization (e.g., Pareto‑efficient site sets), and (iii) planning and policy instruments that operationalize "low‑conflict" development (e.g., designated development areas and program‑level environmental assessment). Results emphasize that land‑use intensity varies strongly by technology and by the metric used; consequently, planning outputs should report both direct and total areas with explicit definitions. The synthesis further shows how strengthened avoidance of high conservation‑value areas, early integration of grid feasibility, and transparent benefit‑sharing and participation mechanisms can reduce conflict without undermining deployment. The paper concludes with best‑practice recommendations and an implementable roadmap for agencies and developers to embed spatial optimization into permitting and long‑term land‑use plans.

Long-term performance predictions for solar-powered alkaline water electrolyzers (AWE) often neglect material degradation, leading to optimistic techno-economic assessments. This paper presents a degradation-aware analysis to identify an optimal operating strategy that maximizes the lifetime value of a standalone PV-battery-AWE system. A comprehensive dynamic model, incorporating a physics-informed degradation mechanism, was used to simulate ten distinct operational power strategies (25 W to 250 W) over a 5-year period. The results reveal a critical trade-off between throughput and longevity. The aggressive 250 W strategy incurred over 14 times more degradation than the 25 W strategy, resulting in a 52% loss in its daily hydrogen production rate by the end of life. While this aggressive strategy yielded the highest cumulative production (107.6 kg) and lifetime revenue ($430.4), it showed severely diminishing returns compared to less intensive strategies. The analysis demonstrates that the optimal strategy is not simply the one with the highest initial output. This study concludes that a degradation-aware approach is essential for accurately forecasting the lifetime value and identifying the most economically sustainable operating strategy for green hydrogen systems.

In agrivoltaic controlled environmental agriculture, semi-transparent photovoltaic (STPV) modules are incorporated into greenhouses to provide sustainable energy for operations. For the first time, this study evaluates the performance of STPV technologies integrated into tomato greenhouses in southern Ontario through experimental agronomic investigations and three energy, economic, and environmental simulation scenarios: i) conventional gas-heated, ii) heat pump-based, and iii) agrivoltaic-heat pump-based greenhouses. STPV configurations tested included crystal silicon with 44% and 69% transparency, as well as with luminescent solar concentrators (53% and 69% transparency), and red and blue thin-film (50% transparency). The agrivoltaic systems maintained stable leaf chlorophyll content and comparable growth trends relative to the control, however 69% transparent crystal silicon PV improved yield the best by 38%. EnergyPlus modeling results indicated that while heating loads were nearly three times higher than cooling loads, replacing the gas heater with a heat pump fully eliminated fossil fuel consumption, and increased electricity use by only about 1.5 times. Integrating the selected 69% STPV system with a heat pump enabled fully electrification of an agrivoltaic greenhouse. The agrivoltaic system supplied approximately 13% of the total annual electricity demand. Although this increased costs 5% relative to subsidized gas, the costs were stabilized and the carbon emissions were reduced by 71.7%, outperforming the heat-pump-only retrofit with 67.9% reductions. Overall, the results demonstrated that rooftop agrivoltaic integration combined with heat pump electrification is a technically feasible and environmentally effective pathway toward low-carbon greenhouse operation in northern climates like Canada.

Africa's renewable energy transition promises to be a game-changer-not just for the continent but for the global fight against climate change. With vast solar, wind, and hydropower potential, Africa could drive its own development while supplying clean energy worldwide. Yet beneath this green vision lies a troubling paradox: while foreign investors and multinational corporations reap massive profits, local communities often see little benefit beyond temporary labor and land leases. This paper examines who truly profits from Africa's renewable energy boom, revealing a landscape where neocolonial economic patterns persist under the moral guise of sustainability. Through an analysis of financial flows, ownership structures, and policy frameworks across key projects-from Egypt's Benban Solar Park to South Africa's Cookhouse Wind Farm-we expose how the current model systematically disadvantages African stakeholders. Foreign entities capture up to 80% of economic value, while local job creation remains minimal and technology transfer disappointingly rare. Yet there is hope. Case studies from Namibia and Bangladesh show that enforceable local content policies, community ownership models, and South-South partnerships can shift this dynamic, ensuring renewable energy becomes a tool for empowerment rather than extraction. This paper not only diagnoses the problem but offers a bold roadmap for reform-prioritizing energy sovereignty, equitable value distribution, and industrial transformation. For policymakers, investors, and activists alike, the stakes could not be higher: will Africa's green transition repeat old injustices, or will it finally break the cycle of resource exploitation? The answer will shape not just the continent's energy future but the very meaning of climate justice in the 21st century.