Renewable Energy Systems (United States)

As global energy demand increases, improving the sustainability and efficiency of energy use becomes a pressing challenge. Solar-based technologies offer significant potential for decentralized energy production due to their modularity and growing availability. This study focuses on the integration of household-scale off-grid photovoltaic (PV) systems with electric thermal storage to reduce residential natural gas consumption—particularly for space heating and domestic hot water production—in the context of Hungary. The research first explored whether average monthly household natural gas consumption can be estimated using publicly available statistical data. Based on the findings, a seasonal consumption profile was constructed for Hungary, enabling the modeling of a 3.2 kWp off-grid PV system combined with a 200-liter water tank equipped with a 2.4 kW heating element. The simulated system was found to generate approximately 3800 kWh of electricity annually, corresponding to a potential amount of nearly 393 m³ of natural gas saved per household. The simulation was conducted on a monthly basis, reflecting the resolution of available consumption data, as more detailed temporal datasets are not publicly accessible. The novelty of the study lies in its application of off-grid PV systems explicitly designed for thermal energy generation, a topic that has received limited attention in the literature, while also developing a data-driven, reproducible national-scale framework that estimates the seasonal gas-saving potential of such PV-to-heat systems based on official statistical data, thereby bridging the gap between high-resolution case studies and aggregated energy policy models. In addition to quantifying natural gas savings, the study emphasizes the problem of seasonal surplus energy and underlines the importance of proper sizing and utilization strategies for off-grid systems. The modeling framework can be adapted to other countries where similar data are available. • This study outlines a data-based framework to assess household gas savings from off-grid PV-to-heat systems in Hungary. • A 3.2 kWp off-grid PV-to-heat system with 200 L storage can cut household gas use by 26 % annually. • The modeled configuration produces ≈ 3800 kWh per year, replacing about 393 m³ of natural gas. • Seasonal imbalance causes ≈ 36 % loss of potential, mainly due to summer PV surplus. • The proposed monthly-scale approach is transparent and transferable to other EU countries.

Hurricanes have significant consequences for ecosystems, potentially disrupting the carbon cycle at both local and regional scales and releasing carbon back into the atmosphere through storm-associated impacts on vegetation and agricultural areas. The present work analyzes the interactions amongst terrestrial carbon fluxes, rainfall, and land cover for three significant hurricanes: Harvey (Texas), Irma (Florida), and Maria (Puerto Rico). This study utilized net ecosystem exchange (NEE) data derived from the Soil Moisture Active Passive (SMAP) NASA satellite mission, which provides global estimates of soil moisture and carbon flux, and analyzed these data for coastal climate zones during the hurricane season. The results were validated using eddy covariance tower-based in-situ CO 2 flux observations during hurricane landfall. Results showed that southern Texas (Harvey) experienced the highest amount of carbon release (0.33 megatons), followed by Florida (Irma) (0.03 megatons) and Puerto Rico (Maria) (0.02 megatons). The land cover products, such as the National Land Cover Dataset (NLCD) and the Copernicus Global Land Service (CGLS), showed overall reductions in land cover in Florida (-1.02%), Texas (-0.97%), and Puerto Rico (-0.46%). Furthermore, vegetation cover changes were estimated using MODIS-derived enhanced vegetation index (EVI), showing major changes over Puerto Rico (-3.81%) and southeast Texas (-2.94%), while normalized difference vegetation index (NDVI) showed more moderate reductions over Puerto Rico (-3.06%), southeast Texas (-1.12%), and Florida (-0.16%). These reductions indicate short-term vegetation stress and decreased photosynthetic activity, which may temporarily reduce carbon uptake, leading affected regions to transition from carbon sinks to temporary carbon sources. These findings highlight hurricanes as significant drivers of short-term carbon emissions and vegetation change. This study enhances understanding of hurricane-associated disturbances in the carbon cycle by examining spatial and temporal variations in carbon fluxes during extreme weather events. • The impacts of hurricanes Harvey, Irma, and Maria (2017) on terrestrial carbon fluxes were assessed. • Carbon release during and after landfall was measured using SMAP-derived NEE and eddy covariance CO 2 flux data. • Hurricane Harvey resulted in the largest carbon emission (0.33 megatons), followed by Irma (0.03 megatons) and Maria (0.02 megatons). • Vegetation loss, derived from MODIS NDVI and land cover change products, was greatest in Texas (6,199.3 km 2 ), then Florida (492.92 km 2 ), and Puerto Rico (14.53 km 2 ). • Vegetation declines led to reduced photosynthetic activity, temporarily turning affected areas from carbon sinks into carbon sources. • Findings highlight hurricanes as significant short-term drivers of carbon emissions and ecosystem disturbance across coastal regions.

Schizophrenia spectrum disorders (SSDs) are managed with pharmacotherapy alongside psychosocial interventions, including cognitive-behavioral therapy for psychosis (CBTp). In routine services, uptake and completion of CBTp remain limited, and participation barriers disrupt initiation, retention, and between-session follow-through. Ketogenic metabolic therapy (KMT) is a dietary intervention intended to induce and maintain nutritional ketosis through carbohydrate restriction and a higher fat dietary pattern, with education, monitoring, and safety management integrated into care. Early reports utilizing KMT in SSDs describe improvements in domains that limit CBTp delivery, including sleep disruption, arousal and distress reactivity, mood instability, cognitive burden, and day to day functioning, while effects on positive symptoms remain uncertain and evidence is methodologically heterogeneous. This perspective article situates KMT within CBTp engagement frameworks and reframes an actionable question for clinical psychologists: whether KMT can reduce participation limiting factors enough to increase CBTp initiation, retention, and completion when psychotherapy is truncated by instability, cognitive burden, distress, or practical barriers. A care pathway model is outlined that organizes participation barriers across entry into therapy and continuity across transitions back to community care, with CBTp indicators proposed for evaluating adjunct impact. If future trials support this adjunct role, KMT may represent a practical strategy for increasing CBTp dose and strengthening access to recovery-oriented gains in coping, stability, and functional outcomes, even when positive symptoms persist or fluctuate. These elements form a clinically grounded conceptual model that specifies measurable indicators and testable decision points for evaluating KMT as an adjunct to CBTp delivery in routine care.

The integration of solar photovoltaic (PV) systems with agricultural activities, known as rooftop agro-voltaic system (RTAVS), offers a promising approach to simultaneously meet energy and food production demands in urban and peri-urban areas. The study evaluates the energy performance and shading effect of a 76.8 kWp rooftop PV system installed at the CAET, DBSKKV, Dapoli, MH, India. The system comprises 240 mono-crystalline modules arranged in 12 strings and was monitored over two experimental periods (October 2022–May 2023 and October 2023–May 2024), covering winter and summer seasons. Energy performance was analyzed and shadow trajectory analysis was conducted using a 3D SketchUp model to simulate seasonal and daily shading patterns, quantifying shadow coverage over the cultivable area. Results demonstrated a strong correlation between solar irradiance and energy generation (R² = 0.984), indicating stable PV system performance under rooftop agro-voltaic conditions, with an estimated annual energy yield of 81,896 kWh. Seasonal shadow coverage ranged from 42.9% in winter to 18.9% in summer, with an overall mean of 30.9%. Moderate shading under RTAVS provided favorable conditions for shade-tolerant crops without compromising PV performance, confirming the technical feasibility of dual land use in urban renewable energy applications. This study presents a combined evaluation of PV performance and shadow trajectory in a rooftop agro-voltaic system, which has received limited attention in previous studies.

The advancement of power electronics in recent years is largely attributed to the development of semiconductor components and new architectures for energy conversion systems. Among these, multicellular converters — which use a series connection of elementary switching cells — have gained significant attention This connection enables higher power processing without increasing stress on the switches, while also improving the output signal quality. However, managing the voltages across floating capacitors in these systems requires precise control. This paper proposes a hybrid modeling and control approach for a multicellular converter with three cells. The hybrid automaton model integrates both continuous and discrete dynamics, making it an effective tool for modeling power electronic systems with Boolean control inputs. Simulation results highlight the effectiveness of the proposed hybrid approach in overcoming voltage control and operational complexities in multicellular converters. By providing a novel solution to voltage control challenges, this modeling framework paves the way for advanced control solutions in power electronics applications. The findings demonstrate notable improvements in efficiency and response times, reinforcing the potential of hybrid modeling in optimizing power electronic systems.

This paper narrates the academic journey of the author from a precarious child to a STEAM learner, crossing up and down moments across the time period. The purpose of this study is to explore how an indigenous child endured the discriminatory burdens as a hegemonic practice of the education system of Nepal. The paper is framed in an autoethnographic research design explaining lived experiences, feelings, and events as the discriminatory burdens, learning struggles, and envision for a better future of school science education. The paper discussed four major themes: research discrimination, research struggles, and envisioning justice-oriented STEAM education. An untouchable school environment and discriminatory responses from the teachers are the major concerns of this paper. Based on the discussion conclusion is drawn that the culturally responsive approaches can address the raised issues in the classroom, and open the floor for the development of local epistemologies.

تستكشف هذه الدراسة حلاً مستداماً لمعالجة عدم التطابق الومني بين العرض والطلب على الكهرباء في منطقة براك الشاطئ من خلال استغلال فائض الكهرباء خلال ساعات انخفاض الطلب لإنتاج الهيدروجين عبر التحليل الكهربائي، ثم استخدام خلايا الوقود لتوليد الكهرباء خلال فترات الذروة. تقيم الدراسة جدوى استخدام نظام يعتمد على الهيدروجين لتحقيق استقرار الشبكة الفرعية دون الحاجة إلى وحدات توليد كهرباء إضافية. ووفقاً للنتائج، يمكن للمحلل الكهربائي إنتاج ما يصل إلى 271.8 متر مكعب من الهيدروجين في الساعة، ويتطلب ذلك حوالي 9.1 ميغاواط من الكهرباء، وهو ما يكفي لتغطية العجز في الطاقة البالغ 6.1 ميغاواط في الشبكة. وبسعة تخزين للهيدروجين تبلغ 421,255 متر مكعب، يمكن لهذا النظام تخزين الهيدروجين لاستخدامه لاحقاً في خلايا الوقود لتغطية العجز البالغ 1689 ميغاواط في الشبكة الفرعية خلال أوقات الذروة. وتشير النتائج إلى أنه من خلال تحسين إنتاج الهيدروجين بالتحليل الكهربائي واستخدام خلايا الوقود لتوليد الكهرباء، يمكن للنظام المقترح توفير 8,424 ميغاواط من الطاقة، مما يغطي فعلياً عجز الشبكة. ولا يقتصر هذا الأسلوب على سد الفجوة بين العرض والطلب فحسب، بل يثبت أيضاً أنه حل فعال من حيث التكلفة والكفاءة لتحقيق استقرار الشبكة دون الحاجة إلى محطات توليد كهرباء إضافية.

تقدم هذه الورقة تحليلاً لنظام طاقة شمسية عائم بقدرة 600 ميجاوات مقترح تركيبه في الخمس، ليبيا. تمت محاكاة النظام باستخدام برنامج PVsyst الإصدار 7.4 مع طريقة التحويل القائمة على طريقة HDKR باستخدام بيانات الطقس من Meteonorm الإصدار 8.1 ومصادر بيانات الطقس Solargis. تظهر نتائج المحاكاة أن النظام سيحقق إنتاجًا سنويًا للطاقة يبلغ 1,346 جيجاوات ساعة/سنة، ونسبة أداء للنظام تبلغ 81.7٪، وإنتاجًا محددًا للطاقة يتراوح بين 1,850 و 2,050 كيلووات ساعة/كيلوواط/سنة مع عامل قدرة يبلغ 25.6٪. سيحقق نظام FPV مكاسب إشعاع خلفي ثنائي الوجه تتراوح بين 3.0 و4.5٪ وسيوفر 1.25 مليون متر مكعب من المياه السطحية سنويًا، ما يعادل قيمة نقدية تتراوح بين 250,000 و625,000 يورو سنويًا. يبلغ إجمالي الاستثمار المطلوب للمشروع ما بين 480 و540 مليون يورو، أي ما يعادل 0.80 إلى 0.90 يورو لكل واط ذروة من التيار المباشر، وينتج عنه تكلفة طاقة متوسطة (LCOE) تتراوح بين 0.055 و 0.065 يورو لكل كيلوواط/ساعة على مدى 25 عامًا ومعدل خصم 5٪. وهذا يتماشى مع LCOE لمحطات الطاقة التقليدية في ليبيا، والتي تتراوح بين 0.07 و 0.10 يورو لكل كيلوواط/ساعة. عند إضافة التكلفة الكربون (SC_CO2 ~70 دولارًا أمريكيًا/طن CO2)، تتراوح تكلفة الطاقة المعدلة بيئيًا بين 0.028 و0.035 يورو/كيلوواط ساعة. تثبت النتائج التي تم الحصول عليها جدوى أنظمة الطاقة الكهروضوئية العائمة (FPV) على نطاق المرافق العامة في ليبيا.

Abstract Traditional agricultural practices have various ecological and environmental challenges. This study examined the effects of rice straw biochar on the physico-chemical properties and water retention capacity of sandy soil under rain-fed lowland rice cultivation using FARO 44 and FARO 52 rice varieties. The study also considered the environmental impact of open-burning, emphasizing the need to transit to cleaner technology. The experiment was conducted using complete randomize experimental design. The biochar-soil mixture was applied at various rates (biochar: 0 g/5 kg, 20 g/5 kg, 60 g/5 kg, 100 g/5 kg, 120 g/5 kg) for the physicochemical characterization and replicated four times. 0 g/5 kg, 20 g/5 kg, 60 g/5 kg and 100 g/5 kg experimental mixture biochar and soil was used for water content and plant growth parameters experiments. The impact of biochar was assessed on various soil properties such as organic matter, soil bulk density, pH, cation exchange capacity (CEC), and macro-nutrients, also data on the plant performance such as tiller number, plant height, root biomass and shoot were collected. Statistical analysis was conducted on the result using analysis of variance, also significant means were separated using Tukey HSD test at 0.05 significance levels. The result revealed that the 100 g/5 kg treatment showed the best output, achieving 97.7% water retention and a pH of 7.00 ± 0.57. Both rice varieties had improved growth and biomass yield, with FARO 44 reaching an average plant height of 2.32 ± 0.46 m. The study also showed a significant reduction in the emission of the GHGS from adoption of biochar technology compared to open field burning method indicating a more environmentally friendly method of disposing the rice straw. These results highlight rice straw biochar as a sustainable soil amendment that improves sandy soil fertility, environmental friendly and enhances rice productivity.

This paper proposes a novel hybrid intelligent control framework integrating a fuzzy supervisory Proportional-Integral-Derivative (PID) controller with Soft Actor-Critic (SAC) reinforcement learning for pitch angle regulation in variable-speed wind turbines. The fuzzy supervisory layer employs seven triangular membership functions for both rotor speed error and its rate of change, resulting in a comprehensive 49-rule inference base that dynamically schedules PID gains to address aerodynamic nonlinearities. The SAC algorithm-operating as an off-policy actor-critic reinforcement learner with entropy maximization-performs online meta-tuning of the fuzzy-scaled gains, enabling continuous adaptation to turbulent wind conditions, parameter uncertainties, and unmodeled disturbances. Practical implementation challenges are mitigated through anti-windup back-calculation to prevent integral saturation and low-pass filtering to suppress derivative kick and measurement noise. Stability of the hybrid closed-loop system is analyzed using Lyapunov methods adapted for reinforcement learning frameworks, ensuring boundedness and convergence. Extensive simulations in MATLAB/Simulink on a 2 MW doubly-fed induction generator (DFIG) wind turbine under realistic IEC-compliant turbulent wind profiles demonstrate superior performance: up to 25% improvement in annual energy production (AEP), 22% reduction in fatigue equivalent loads, and markedly smoother rotor speed tracking compared to conventional PID, standalone fuzzy PID, and baseline actor-critic methods. This work extends the authors' prior SAC-based meta-tuning framework from robotic manipulator trajectory tracking [1] to renewable energy systems, offering a high-performance, learning-enabled solution for modern wind farms. achieving inference times under 5 ms on embedded platforms such as NVIDIA Jetson Nano with TensorRT optimization