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Prospects and Obstacles Associated with Community Solar and Wind Farms in Jordan’s Suburban Areas

Jordan faces significant, immediate challenges of enhancing energy security while mitigating greenhouse gas emissions. One of the most promising approaches to achieve sustainable development, energy security, and environmental conservation is to increase the integration of renewable energy into electricity generation. To this end, the Jordanian government aims to expand investments in the green energy sector, with solar and wind energy expected to play a crucial role in meeting energy demands and promoting environmental sustainability. This paper aims to examine the distinct dynamics, challenges, obstacles, and potential solutions related to establishing community solar and wind farms in suburban areas of Jordan. It seeks to highlight the opportunities and barriers influencing the adoption of sustainable energy in the country. Evaluation results from engaging 320 key stakeholders were obtained through a questionnaire, and after comprehensive analysis, it became evident that the benefits and positive aspects of solar and wind farms outweigh their drawbacks and obstacles. These insights can be useful in guiding policies and practices to make renewable energy community projects a reality within Jordan’s suburban areas. Additionally, the findings may serve as a valuable benchmark for other regions facing similar challenges in their pursuit of a sustainable energy future.

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New Decomposition Models for Hourly Direct Normal Irradiance Estimations for Southern Africa

This research develops and validates new decomposition models for hourly direct Normal Irradiance (DNI) estimations for Southern African data. Localised models were developed using data collected from the Southern African Universities Radiometric Network (SAURAN). Clustered areas within Southern Africa were identified, and the developed cluster decomposition models highlighted the potential advantages of grouping data based on shared geographical and climatic attributes. This clustering approach could enhance decomposition model performance, particularly when local data are limited or when data are available from multiple nearby stations. Further, a regional Southern African decomposition model, which encompasses a wide spectrum of climatic regions and geographic locations, exhibited notable improvements over the baseline models despite occasional overestimation or underestimation. The results demonstrated improved DNI estimation accuracy compared to the baseline models across all testing and validation datasets. These outcomes suggest that utilising a localised model can significantly enhance DNI estimations for Southern Africa and potentially for developing similar models in diverse geographic regions worldwide. The overall metrics affirm the substantial advancement achieved with the regional model as an accurate decomposition model representing Southern Africa. Two stations were used as a validation study, as an application example where no localised model was available, and the cluster and regional models both outperformed the comparative decomposition models. This study focused on validating the model for hourly DNI in Southern Africa within a range of Kt-intervals from 0.175 to 0.875, and the range could be expanded and validated for future studies. Implementing accurate decomposition models in developing countries can accelerate the adoption of renewable energy sources, diminishing reliance on coal and fossil fuels.

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A Multi-Stage Approach to Assessing the Echo-Tech Feasibility of a Hybrid SAM-CREST Model for Solar PV Power Plants in Maryland, USA

Maryland is actively working towards doubling its Renewable Portfolio Standard (RPS) target, aiming to increase the share of renewable energy from 25% by 2020 to 50% by 2030. Furthermore, Maryland stands out as a state that strongly supports solar initiatives, offering incentives and specialized programs to assist residents in adopting solar energy solutions. The paper presents a multi-stage approach: Stage 1—Location Selection Process, Stage 2—Technical Feasibility Study, and Stage 3—Economical Feasibility Study. In Stage 1, the study focuses on three potential solar farm locations in Maryland: Westover, Princess Anne, and Eden. Stages 2 and 3 involve a feasibility assessment with detailed technical analysis using the NREL System Advisor Model (SAM) and PVWatts to determine monthly power to the grid and Energy Yield. Subsequently, economic feasibility is assessed using the NREL Clean Renewable Energy Estimation Simulation Tool (CREST), focusing on competitive levelized costs of energy (LCOE), payback time, and cumulative cash flows. Results indicate that all three locations exhibit promising solar irradiance levels, system outputs, and potential energy yields. Due to high solar irradiation, the Westover area has the highest energy yield at 1583.13 kWh/kW, while Princess Anne boasts the highest system output at 333.59 GWh. The economic evaluation suggests that all three locations become profitable within a two-year payback time, with competitive levelized costs of energy (LCOE). Westover emerges as the most cost-effective option at 5.99 cents/kWh, attributed to its higher solar irradiation values and energy yield compared to Princess Anne and Eden. Cumulative cash flows provide insights into long-term profitability, with Princess Anne, MD, having the highest Cumulative Cash Flow over 25 years at $183,383,304. By evaluating technical and economic aspects, this feasibility study offers quantitative insights to guide decision-making for the installation of Solar PV, considering both technological and economic feasibility.

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A Novel Statistical Framework for Optimal Sizing of Grid-Connected Photovoltaic–Battery Systems for Peak Demand Reduction to Flatten Daily Load Profiles

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. However, the PV installation should provide financial benefits for the utilities. Considering that the utility companies often incur costs for both energy and peak demand, PV installations should aim to reduce both energy and peak demand charges. Although PV systems can reduce energy needs during the day, their effectiveness in reducing peak demand, particularly in the early morning and late evening, is limited, as PV generation is zero or negligible at those times. To address this limitation, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV and the accompanying battery remains a challenge. While valuable optimization models have been developed to determine the optimal size of PV–battery systems, a certain gap remains where peak demand reduction has not been sufficiently addressed in the optimization process. Recognizing this gap, this study proposes a novel statistical model to optimize PV–battery system size for peak demand reduction. The model aims to flatten 95% of daily peak demands up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model’s effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV–battery system successfully flattens 95% of daily peak demand with a selected threshold of 2000 kW, yielding a financial benefit of USD 812,648 over 20 years.

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Development of n-Type, Passivating Nanocrystalline Silicon Oxide Films via Plasma-Enhanced Chemical Vapor Deposition

Nanocrystalline silicon oxide (nc-SiOx:H) is a multipurpose material with varied applications in solar cells as a transparent front contact, intermediate reflector, back reflector layer, and even tunnel layer for passivating contacts, owing to the easy tailoring of its optical properties. In this work, we systematically investigate the influence of the gas mixture (SiH4, CO2, PH3, and H2), RF power, and process pressure on the optical, structural, and passivation properties of thin n-type nc-SiOx:H films prepared in an industrial, high-throughput, plasma-enhanced chemical vapor deposition (PECVD) reactor. We provide a detailed description of the n-type nc-SiOx:H material development using various structural and optical characterization techniques (scanning electron microscopy (SEM), energy dispersive X-ray (EDX), Raman spectroscopy, and spectroscopic ellipsometry) with a focus on the relationship between the material properties and the passivation they provide to n-type c-Si wafers characterized by their effective carrier lifetime (τeff). Furthermore, we also outline the parameters to be kept in mind while developing different n-type nc-SiOx:H layers for different solar cell applications. We report a tunable optical gap (1.8–2.3 eV) for our n-type nc-SiOx:H films as well as excellent passivation properties with a τeff of up to 4.1 ms (implied open-circuit voltage (iVoc)~715 mV) before annealing. Oxygen content plays an important role in determining the crystallinity and hence passivation quality of the deposited nanocrystalline silicon oxide films.

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Immersive Learning in Photovoltaic Energy Education: A Comprehensive Review of Virtual Reality Applications

This paper presents a comprehensive and systematic review of virtual reality (VR) as an innovative educational tool specifically for solar photovoltaic energy systems. VR technology, with its immersive and interactive capabilities, offers a unique platform for in-depth learning and practical training in the field of solar energy. The use of VR in this context not only enhances the understanding of solar photovoltaic (PV) systems but also provides a hands-on experience that is crucial for developing the necessary skills in this rapidly evolving field. Among the 6814 articles initially identified, this systematic review specifically examined 15 articles that focused on the application of VR in PV education. These selected articles demonstrate VR’s ability to accurately simulate real-world environments and scenarios related to solar energy, providing an in-depth exploration of its practical applications in this field. By offering a realistic and detailed exploration of PV systems, VR enables learners to gain a deeper understanding of harnessing, managing and using such a vast energy resource. The paper further discusses the implications of employing VR in educational settings, highlighting its potential to change the way solar energy professionals are trained, thereby contributing significantly to the acceleration of photovoltaic technology adoption and its integration into sustainable energy solutions.

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A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape.

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