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Featured Application: This paper presents a methodology for the implementation of edge intelligence for predicting solar panel performances on wireless sensor nodes. The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper. [ABSTRACT FROM AUTHOR]

The global interest in environmental issues and sustainable energy has propelled extensive research in photovoltaic (PV) technologies. Brazil has emerged as one of the top ten solar energy producers and flexible PV suppliers in the world. In this context, organic photovoltaic cells (OPVs) have garnered attention due to their flexibility and ability to integrate into various surfaces, albeit facing challenges regarding lifespan and efficiency compared to silicon cells. This review examines the current state of research on OPVs and thin-film solar technologies, employing the systematic literature review methodology Proknow-C. The review includes an analysis of raw materials such as conductive polymers, fabrication processes including film deposition and encapsulation, and technological advancements that enhance the efficiency and stability of OPVs. Utilizing the Proknow-C methodology, a bibliographic portfolio was constructed to identify the current state of research in this field. Of 268 papers from major scientific databases, only 30 were deemed relevant to the theme, highlighting a significant research gap. This paper is a valuable resource for researchers, providing an updated overview and a foundation for future investigations in organic photovoltaics. The results emphasize the importance of materials such as conductive polymers and donor–acceptor molecules and the role of nanotechnology in advancing OPVs. Innovations in manufacturing techniques, such as inkjet and blade coating-based printing techniques, are shown to increase efficiency by providing precise control over film thickness and uniformity and reducing costs through lower material waste. Overall, this review highlights the necessity of further exploration and collaboration within the scientific community to address the challenges and propel advancements in organic photovoltaic cells. These advancements are crucial for transitioning to cleaner energy sources, reinforcing the ongoing significance of thin-film technologies in energy innovation. [ABSTRACT FROM AUTHOR]

The global trend of reducing the "carbon footprint" has influenced the dynamic development of projects that use renewable energy sources, including the development of solar energy in large solar power plants. Consequently, there is an increasingly pronounced need in scientific circles to consider the impact these projects have on space and the environment. The fact that international financial institutions consider environmental effect to be a significant factor when funding solar energy projects is one of the main reasons this topic is so important in professional circles, particularly among solar energy investors. This paper highlights the fact that solar power plants can have both positive and negative impacts on space and the environment. Those impacts need to be defined in order to choose optimal spatial and territorial solutions that ensure preventive planning and active environmental protection. In the process, the application of strategic environmental assessment (SEA) in the planning and spatial organization of solar power plants becomes important. SEA is characterized by a holistic approach where complex interactions and correlations in the location of planned implementation of the solar power plant can be understood at the earliest stage of project development. By doing this, it is possible to prevent all potential risks that may emerge in the project's later stages of implementation, which is favorable both from the aspect of effective environmental protection and from the point of view of investors investing in solar power plant projects. Optimal solutions that bring about the basic role of SEA are sought primarily in the analysis of the spatial relations of the solar power plant with regard to land, biodiversity, landscape, and basic environmental factors, which is particularly highlighted in the paper. Also, the basic methodological concept applied in SEA is demonstrated, combining different methodological approaches and methods for impact assessment, as part of a unique semi-quantitative method of multi-criteria evaluation of planning solutions. [ABSTRACT FROM AUTHOR]

Water scarcity poses significant challenges in arid regions like the Middle East and North Africa (MENA) due to constant population growth, considering the effects of climate change and water management aspects. The desalination technologies face problems like high energy consumption, high investment costs, and significant environmental impacts by brine discharge. This paper researches the relationships among water scarcity, energy-intensive desalination, and the development of renewable energy in MENA, with a particular focus on the Gulf Cooperation Council (GCC) countries. It examines innovations in solar-powered desalination, considering both solar photovoltaic (PV) and solar thermal technologies, in combination with traditional thermal desalination methods such as multi-effect distillation (MED) and multi-stage flash (MSF). The environmental impacts associated with desalination by brine discharge are also discussed, analyzing innovative technological solutions and avoidance strategies. Utilizing bibliometrics, this report provides a comprehensive analysis of scientific literature for the assessment of the research landscape in order to recognize trends in desalination technologies in the MENA region, providing valuable insights into emerging technologies and research priorities. Despite challenges such as high initial investment costs, technical complexities, and limited funding for research and development, the convergence of water scarcity and renewable energy presents significant opportunities for integrated desalination systems in GCC countries. Summarizing, this paper emphasizes the importance of interdisciplinary approaches and international collaboration by addressing the complex challenges of water scarcity and energy sustainability in the MENA region. By leveraging renewable energy sources and advancing desalination technologies, the region can achieve water security while mitigating environmental impacts and promoting economic development. [ABSTRACT FROM AUTHOR]

Renewable Energy Sources (RESs) such as hydro, wind, and solar are merging as preferred alternatives to fossil fuels. Among these RESs, solar energy is the most ideal solution; it is gaining extensive interest around the globe. However, due to solar energy's intermittent nature and sensitivity to environmental parameters (e.g., irradiance, dust, temperature, aging and humidity), real-time solar plant monitoring is imperative. This paper's contribution is to compare and analyze current IoT trends and propose future research directions. As a result, this will be instrumental in the development of low-cost, real-time, scalable, reliable, and power-optimized solar plant monitoring systems. In this work, a comparative analysis has been performed on proposed solutions using the existing literature. This comparative analysis has been conducted considering five aspects: computer boards, sensors, communication, servers, and architectural paradigms. IoT architectural paradigms employed have been summarized and discussed with respect to communication, application layers, and storage capabilities. To facilitate enhanced IoT-based solar monitoring, an edge computing paradigm has been proposed. Suggestions are presented for the fabrication of edge devices and nodes using optimum compute boards, sensors, and communication modules. Different cloud platforms have been explored, and it was concluded that the public cloud platform Amazon Web Services is the ideal solution. Artificial intelligence-based techniques, methods, and outcomes are presented, which can help in the monitoring, analysis, and management of solar PV systems. As an outcome, this paper can be used to help researchers and academics develop low-cost, real-time, effective, scalable, and reliable solar monitoring systems. [ABSTRACT FROM AUTHOR]

This paper investigates whether renewable energy influences economic growth and happiness. Using panel data from 25 European Union countries for the period 2012–2022, this study employs a panel model for estimation with fixed and random effects, and robust HAC standard errors. According to the research results, in general, the growing share of renewable energy in the energy mix has a positive impact on economic growth and the happiness of citizens. However, detailed research has shown that this effect depends on the type of energy; a significant positive impact was recorded only in solar share energy, wind share energy and economic growth. However, almost all types of renewable energy were included, i.e., biofuel, hydro, solar and other renewable share energy, and all had a significantly positive impact on the level of happiness. The exception was wind share energy, which showed a significant negative impact. The research findings of this paper provide empirical support for promoting renewable energy, which is positive both for economies and the happiness of citizens. It is one of the main aspects of sustainable economic growth. [ABSTRACT FROM AUTHOR]

The goal of increasing fuel efficiency and decreasing greenhouse gas (GHG) emissions has increased interest in the application of renewable energy sources and the usage of new technologies in the maritime industry. In order to implement the most suitable source, factors such as voyage duration, storage availability, and the condition of existing vessels as well as those that are still under construction should be taken into account. Propeller optimization is proposed as a long-term solution. This paper investigates the environmental aspects of propeller optimization, focusing on its potential to reduce ship vibrations fuel consumption, and, therefore, the ship's carbon footprint. The case study presents propeller optimization on a Ro-Ro passenger ship. The data collected during sea trials before and after propeller optimization will be compared. Expected fuel oil consumption will be correlated to the CO2 emission reduction. Besides propeller optimization, the paper performs a SWOT (strengths, weaknesses, opportunities, threats) analysis comparing it with solar and wind power applications on ships. [ABSTRACT FROM AUTHOR]

Access to clean energy remains a major issue in developing countries, particularly Sub-Saharan Africa, despite successive policies and the assistance of international institutions or organizations. The United Nations (UN) launched some of the most ambitious initiatives with the Millennium Development Goals and, more recently, the Sustainable Development Goals and Power Africa, a United States (US) government initiative. Sub-Saharan Africa has an important potential in renewable energy for both biogas and solar photovoltaic energy, but they remain underexploited. This paper presents the challenges of access to clean energy in developing countries and the failure of remedial policies mostly based on public–private partnerships (PPPs) in the context of endemic poverty of rural populations. In addition, the development of modern energy technologies remains very limited. Appropriate reforms should be carried out to change the paradigm and allow universal access to clean energy. This paper also addresses the different structural barriers that hinder access to technology in Sub-Saharan Africa and the consequences of access to clean energy in the context of poverty. [ABSTRACT FROM AUTHOR]

Reduction or elimination of reliance on traditional fossil fuels and of the emission of greenhouse gases and pollutants into the environment are affecting energy technologies, systems, and applications. In this context, one potential approach to achieving sustainability, decarbonization, and ensuring the energy and economic viability of existing and future energy systems involves adopting one or more renewable sources. The presented paper concentrates on examining the performance of a small-scale hybrid renewable polygeneration system. This system utilizes biogas produced through anaerobic digestion, which is then supplied to an internal combustion engine, along with solar energy converted into electrical energy by photovoltaic modules and wind energy harnessed through a wind turbine. A small-scale user, represented by residential buildings and a zootechnical farm with heating, cooling, and electrical energy demands, serves as the case study. TRNSYS software is employed to design and model the system, considering realistic assumptions about technical aspects and user energy requirements. The investigation involves analyzing the system's operation, considering both energy and economic perspectives. The paper discusses the pros and cons of combining biogas, solar, and wind energy in the proposed hybrid system under the considered case study. Despite non-satisfactory economic profitability without incentives, the proposed system allows one to save significant amounts of primary energy and carbon dioxide equivalent emissions. [ABSTRACT FROM AUTHOR]

As electricity demand increases, especially in transportation, renewable sources such as solar energy become more important. The direct integration of solar energy in rail transportation mostly involves utilizing station roofs and track side spaces. This paper proposes a novel approach by proposing the integration of photovoltaic systems directly on the roofs of trains to generate clean electricity and reduce dependence on the main grid. Installing solar photovoltaic (PV) systems on train rooftops can reduce energy costs and emissions and develop a more sustainable and ecological rail transport system. This research focuses on the Milan Cadorna-Saronno railway line, examining the feasibility of installing PV panels onto train rooftops to generate power for the train's internal consumption, including lighting and air conditioning. In addition, it is a solution to reduce the power absorbed by the train from the main supply. Simulations conducted using PVSOL software 2023 (R7) indicate that equipping a train roof with PV panels could supply up to almost 10% of the train's auxiliary power needs, equating to over 600 MWh annually. Implementing the suggested system may also result in a decrease of more than 27 tons of CO2 emissions per year for one train. To optimize the performance of PV systems and maximize power output, the gravitational search algorithm (GSA) as an evolutionary-based method is proposed alongside a DC/DC boost converter and its performance is compared with two other main maximum power point tracking (MPPT) methods of perturb and observe (PO), and incremental conductance (INC). The accuracy of the suggested algorithm was confirmed utilizing MATLAB SIMULINK R2023b, and the results were compared with those of the PO and INC algorithms. The findings indicate that the GSA performs better in terms of accuracy, while the PO and INC algorithms demonstrate greater robustness and dynamic response. [ABSTRACT FROM AUTHOR]

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications. [ABSTRACT FROM AUTHOR]

Hydrogen produced from renewable energy sources is a valuable energy carrier for linking growing renewable electricity generation with the hard-to-abate sectors, such as cement, steel, glass, chemical, and ceramics industries. In this context, this paper presents a new model of hydrogen production based on solar photovoltaics and wind energy with application to a real-world ceramics factory. For this task, a novel multipurpose profit-maximizing model is implemented using GAMS. The developed model explores hydrogen production with multiple value streams that enable technical and economical informed decisions under specific scenarios. Our results show that it is profitable to sell the hydrogen produced to the gas grid rather than using it for self-consumption for low-gas-price scenarios. On the other hand, when the price of gas is significantly high, it is more profitable to use as much hydrogen as possible for self-consumption to supply the factory and reduce the internal use of natural gas. The role of electricity self-consumption has proven to be key for the project's profitability as, without this revenue stream, the project would not be profitable in any analysed scenario. [ABSTRACT FROM AUTHOR]

Aiming to mitigate the impact of power fluctuation caused by large-scale renewable energy integration, coupled with a high rate of wind and solar power abandonment, the multi-objective optimal dispatching of a cascade hydro–wind–solar–thermal hybrid generation system with pumped storage hydropower (PSH) is proposed in this paper. Based on the proposed system, the scheduling operation strategy takes into account the complex restrictions of cascade hydropower as well as the flexibility of the PSH. According to various scenarios, the NSGA-II approach is adopted to address the optimization problem, minimizing the system's residual load variation and operation cost. The Pareto solution sets are contrasted and evaluated, applying the TOPSIS with CRITIC weighting. Additionally, the scheduling output of thermal power, cascade hydropower, and PSH is given in terms of different scenarios. The results demonstrate that the allocation of PSH to a hybrid energy system can significantly reduce the operation cost and the fluctuation in the residual load. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement. [ABSTRACT FROM AUTHOR]

The integration of renewable energy is rapidly leading the existing grid systems toward modern hybrid power systems. These hybrid power systems are more complex due to the random and intermittent nature of RE and involve numerous operational challenges. This paper presents the operational model for solar integrated power systems to address the issues of economical operation, reliable solar share, energy deficit in case of contingency events, and the allocation of system spinning reserve. A mixed-integer optimization is formulated to minimize the overall cost of the system operation and to maximize the solar share under robust system spinning reserve limits as well as various other practical constraints. A Pareto-optimal solution for the maximization of the number of solar power plants and minimization of the solar cost is also presented for reliable solar share. Further, a decomposition framework is proposed to split the original problem into two sub-problems. The solution of joint optimization is obtained by exploiting a Lagrange relaxation method, a binary search Lambda iteration method, system spinning reserve analysis, and binary integer programming. The proposed model was implemented on an IEEE-RTS 26 units system and 40 solar plants. [ABSTRACT FROM AUTHOR]

The massive grid integration of renewable energy necessitates frequent and rapid response of hydropower output, which has brought enormous challenges to the hydropower operation and new opportunities for hydropower development. To investigate feasible solutions for complementary systems to cope with the energy transition in the context of the constantly changing role of the hydropower plant and the rapid evolution of wind and solar power, the short-term coordinated scheduling model is developed for the wind–solar–hydro hybrid pumped storage (WSHPS) system with peak shaving operation. The effects of different reservoir inflow conditions, different wind and solar power forecast output, and installed capacity of pumping station on the performance of WSHPS system are analyzed. The results show that compared with the wind–solar–hydro hybrid (WSH) system, the total power generation of the WSHPS system in the dry, normal, and wet year increased by 10.69%, 11.40%, and 11.27% respectively. The solar curtailment decreased by 68.97%, 61.61%, and 48.43%, respectively, and the wind curtailment decreased by 76.14%, 58.48%, and 50.91%, respectively. The high proportion of wind and solar energy connected to the grid in summer leads to large net load fluctuations and serious energy curtailment. The increase in the installed capacity of the pumping station will promote the consumption of wind and solar energy in the WSHPS system. The model proposed in this paper can improve the operational flexibility of hydropower station and promote the consumption of wind and solar energy, which provides a reference for the research of cascade hydropower energy storage system. [ABSTRACT FROM AUTHOR]

As the demand for renewable energy sources continues to increase, solar energy is becoming an increasingly popular option. Therefore, effective training in solar energy systems design and operation is crucial to ensure the successful implementation of solar energy technology. To make this training accessible to a wide range of people from different backgrounds, it is important to develop effective and engaging training methods. Immersive virtual reality (VR) has emerged as a promising tool for enhancing solar energy training and education. In this paper, a unique method is presented to evaluate the effectiveness of an immersive VR experience for solar energy systems design using a multi-modal approach that includes a detailed analysis of user engagement. To gain a detailed analysis of user engagement, the VR experience was segmented into multiple scenes. Moreover, an eye-tracker and wireless wearable sensors were used to accurately measure user engagement and performance in each scene. The results demonstrate that the immersive VR experience was effective in improving users' understanding of solar energy systems design and their ability to perform complex tasks. Moreover, by using sensors to measure user engagement, specific areas that required improvement were identified and insights for enhancing the design of future VR training experiences for solar energy systems design were provided. This research not only advances VR applications in solar energy education but also offers valuable insights for designing effective and engaging training modules using multi-modal sensory input and real-time user engagement analytics. [ABSTRACT FROM AUTHOR]

Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses' microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation. [ABSTRACT FROM AUTHOR]

Egypt has the potential to generate a significant amount of energy from renewable technologies, in particular solar PV, concentrated solar power (CSP), and onshore and offshore wind. The energy sector is reliant on fossil fuels, particularly natural gas, for electricity production and is at risk of locking itself into a high carbon pathway. Globally, reducing greenhouse gas (GHG) emissions associated with national energy sectors is a target outlined in the UN's Paris Agreement. To reduce carbon dioxide (CO2) emissions associated with a higher dependence on fossil fuels, Egypt must consider upscaling renewable energy technologies (RETs) to achieve a clean energy transition (CET). This research modelled six scenarios using clicSAND for OSeMOSYS to identify the technologies and policy target improvements that are needed to upscale RETs within Egypt's energy sector. The results showed that solar PV and onshore wind are key technologies to be upscaled to contribute towards Egypt's CET. The optimal renewable target is the International Renewable Energy Agency's (IRENA) target of 53% of electricity being sourced from RETs by 2030, which will cost USD 16.4 billion more up to 2035 than Egypt's current Integrated Sustainable Energy Strategy (ISES) target of 42% by 2035; it also saves 732.0 MtCO2 over the entire modelling period to 2070. Socio-economic barriers to this transition are considered, such as recent discoveries of natural gas reserves combined with a history of energy insecurity, political instability impacting investor confidence, and a lack of international climate funding. The paper concludes with policy recommendations that would enable Egypt to progress towards achieving a CET. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive study on the implementation of photovoltaic (PV) energy systems at Al-Abrar Mosque in Saudi Arabia. The primary objective was to explore optimal regional solar power strategies. By synergistically integrating technical evaluations of the PV system with economic analyses, including the payback period and levelized cost of energy (LCOE), alongside an investigation of net metering and net billing scenarios, we delineated a pathway toward achieving net zero billing for the mosque's energy requirements. This study examined two scenarios: Scenario I involved net metering, while Scenario II explored net billing. Our theoretical and simulation results, derived from detailed analyses conducted using PVsyst software, unequivocally demonstrated the superiority of net metering for this specific application. With net metering, the mosque's energy needs can be efficiently met using minimal infrastructure—comprising only 34 photovoltaic modules and a single inverter. In contrast, net billing requires significantly higher resource demands, underscoring the economic and spatial advantages of net metering. Additionally, the payback period for Scenario I is 7.9 years, while for Scenario II, it extends to 87 years. Through rigorous simulations, this study reaffirmed the practicality and feasibility of the net metering approach within the context of Saudi Arabia. Furthermore, our research provides actionable insights for implementing sustainable solutions at specific sites, such as the Al-Abrar Mosque, and contributes to advancing renewable energy knowledge in the region. [ABSTRACT FROM AUTHOR]

As offline control photovoltaic (PV) plants are not equipped with online communication and remote control systems, they cannot adjust their power in real-time. Therefore, in a distribution network saturated with offline control PVs, the distribution system operator (DSO) should schedule the distributed energy resources (DERs) considering the uncertainty of renewable energy to prevent curtailment due to overvoltage. This paper presents a day-ahead network operation strategy using a mobile energy storage system (MESS) and offline control PVs to minimize power curtailment. The MESS model efficiently considers the transportation time and power loss of the MESS, and models various operating modes, such as the charging, discharging, idle, and moving modes. The optimization problem is formulated based on mixed-integer linear programming (MILP) considering the spatial and temporal operation constraints of MESSs and is performed using chanced constrained optimal power flow (CC-OPF). The upper limits for offline control PVs are set based on the probabilistic approach, thus mitigating overvoltage due to forecasting errors. The proposed operation strategy was tested in the IEEE 33-node distribution system coupled with a 15-node transportation system. The test results show the effectiveness of the proposed method for minimizing curtailment in offline control PVs. [ABSTRACT FROM AUTHOR]

Utilizing renewable energy (RE) sources can provide a realistic answer to the problem of many nations' energy needs. This paper reviews the current status of using RE to produce electricity in the Kingdom of Saudi Arabia (KSA). The primary aim of the review is to identify and analyze the solar- and wind-energy utilization, problems, and future trends in KSA while taking into account all relevant aspects, associated development hurdles, and suggested solutions. All involved parties will receive useful results and conclusions from a thorough examination and analysis of the pertinent literature that can assist them in understanding the potential of solar and wind energy, considering all the linked issues. [ABSTRACT FROM AUTHOR]

The energy sector is the main source of greenhouse gases, so it has the highest potential for improvement. The improvements can be achieved by generating energy from renewable sources. It is necessary to combine production from renewable sources with storage systems. Thermal energy storage using concentrated solar power systems is a promising technology for dispatchable renewable energy that can guarantee a stable energy supply even in remote areas without contributing to greenhouse gas emissions during operation. However, it must be emphasised that greenhouse gases and other impacts can occur during the production process of concentrating solar system components. This paper analyses the receiver design to produce thermal energy for the existing CSP dish plant at the Energy Center of the Politecnico di Torino. The plant is designed to produce electrical energy in the spring and summer periods. In addition to this energy production, the CSP can be adopted to produce thermal energy, through hot water, during the less favourable periods of the year in terms of global solar radiation. The surface heat flux is calculated in the first part of the analysis to obtain the maximum internal temperature in the receiver, which is 873.7 °C. This value is a constraint for the choice of material for the solar receiver. A life cycle assessment is performed to compare the emissions generated during the production of the main components of the CSP system with the emissions generated by the methane-fuelled water heater to produce the same amount of thermal energy. It can be concluded that the production of the main components of the CSP system results in lower greenhouse gas emissions than the operational phase of a conventional system. Given the assumptions made, the utilization of methane leads to the emission of approximately 12,240 kg of CO2, whereas the production of the CSP system results in emissions totalling 5332.8 kg of CO2 equivalent [ABSTRACT FROM AUTHOR]

In the context of an insulated area with a subtropical climate, such as La Réunion island, it is crucial to reduce the energy consumption of buildings and develop local renewable energy sources to achieve energy autonomy. Direct current (DC) nanogrids could facilitate this by reducing the energy conversion steps, especially for solar energy. This article presents the deployment and efficiency evaluation of a 48 VDC low-voltage direct current (LVDC) nanogrid, from conception to real-world installation within a company. The nanogrid consists of a photovoltaic power plant, a lithium–iron–phosphate (LFP) battery, and DC end-use equipment, such as LED lighting and DC fans, for two individual offices. For identical test conditions, which are at an equivalent cabling distance and with the same final power demand, the total power consumed by the installation is measured for several stages from 50 to 400 W, according to a 100% DC configuration or a conventional DC/AC/DC PV configuration incorporating an inverter and AC/DC converter. The methodology used enables a critical view to be taken of the installation, assessing both its efficiency and its limitations. Energy savings of between 23% and 40% are measured in DC for a power limit identified at 150 W for a distance of 25 m. These results show that it is possible to supply 48 VDC in an innovative way to terminal equipment consuming no more than 100 W, such as lighting and air fans, using the IEEE 802.3 bt power over ethernet (PoE) protocol, while at the same time saving energy. The nanogrid hardware and software infrastructure, the methodology employed for efficiency quantification, and the measurement results are described in the paper. [ABSTRACT FROM AUTHOR]

The energy sector faces a pressing need for significant transformation to curb CO2 emissions. For instance, Czechia and Germany have taken steps to phase out fossil thermal power plants by 2038, opting instead for a greater reliance on variable renewable energy sources like wind and solar power. Nonetheless, thermal power plants will still have roles, too. While the conventional multistage axial turbine design has been predominant in large-scale power plants for the past century, it is unsuitable for small-scale decentralized projects due to complexity and cost. To address this, the study investigates less common turbine types, which were discarded as they demonstrated lower efficiency. One design is the Elektra turbine, characterized by its velocity compounded radial re-entry configuration. The Elektra turbine combines the advantages of volumetric expanders (the low rotational speed requirement) with the advantages of a turbine (no rubbing seals, no lubrication in the working fluid, wear is almost completely avoided). Thus, the research goal of the authors is the implementation of a 10 kW-class ORC turbine driving a cost-effective off-the-shelf 3000 rpm generator. The paper introduces the concept of the Elektra turbine in comparison to other turbines and proposes this approach for an ORC working fluid. In the second part, the 1D design and 3D–CFD optimization of the 7 kW Elektra turbine working with Hexamethyldisiloxane (MM) is performed. Finally, CFD efficiency characteristics of various versions of the Elektra are presented and critically discussed regarding the originally defined design approach. The unsteady CFD calculation of the final Elektra version showed 46% total-to-static isentropic efficiency. [ABSTRACT FROM AUTHOR]

This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model's predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server. [ABSTRACT FROM AUTHOR]

Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model. [ABSTRACT FROM AUTHOR]

Integrating renewable energy sources (RES), such as photovoltaic (PV) systems, into power system networks increases uncertainty, leading to practical challenges. Therefore, an accurate photovoltaic (PV) power prediction model is required to provide essential data that supports smooth power system operation. Hence, the work presented in this paper compares and discusses the results of different machine learning (ML) techniques in predicting the power produced by the 300 MW Sakaka PV Power Plant in the north of Saudi Arabia. The validation of the presented work is performed using real-world operational data obtained from the specified solar farm. Several performance measures, including accuracy, precision, recall, F1 Score, and mean square error (MSE), are used in this work to evaluate the performance of the different ML approaches and determine the most precise prediction model. The obtained results show that the Support Vector Machine (SVM) with a Radial basis function (RBF) is the most effective approach for optimizing solar power prediction in large-scale solar farms. [ABSTRACT FROM AUTHOR]

Users ought to decide how to operate and manage power systems in order to achieve various goals. As a result, many strategies have been developed to aid in this regard. Optimal power flow management is one such strategy that assists users in properly operating and managing the supply and demand of power in an optimal way under specified constraints. However, in-depth research on optimal power flow management is yet to be explored when it comes to the supply and demand of power for the bulk of standalone renewable energy systems such as solar photovoltaics, especially when it comes to specific applications such as powering military soldier-level portable electronic devices. This paper presents an optimal power flow management modelling and optimisation approach for solar-powered soldier-level portable electronic devices. The OPTI toolbox in MATLAB is used to solve the formulated nonlinear optimal power flow management problem using SCIP as the solver. A globally optimal solution was arrived at in a case study in which the objective function was to minimise the difference between the power supplied to the portable electronic device electronics and the respective portable electronic device power demands. This ensured that the demand for solar-powered soldier-level portable electronic devices is met at all times in spite of the prohibitive case scenarios' circumstances under the given constraints. This resolute approach underscores the importance placed on satisfying the demand needs of the specific devices while navigating and addressing the limitations posed by the existing conditions or constraints. Soldiers and the solar photovoltaic user fraternity at large will benefit from this work as they will be guided on how to optimally manage their power systems' supply and demand scenarios. The model developed herein is applicable to any demand profile and any number of portable electronic device and is adaptable to any geographical location receiving any amount of solar radiation. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model. [ABSTRACT FROM AUTHOR]

The modernization, efficiency, and decarbonization of the energy supply systems are among the new challenges to be faced in the coming decades to achieve the targets and objectives dictated by European strategic policies. Despite the countless benefits related to renewable energy sources (RES) integration, this brings key challenges to the power system, such as the risk of imbalance between energy generation and demand, sudden changes in flows in transmission lines with a need for expensive and time-consuming upgrades, and the withdrawal of conventional generation systems with consequent demands for new solutions and innovation to support grid services. A potential solution to limit the huge intermittence and fluctuation in power generation from RES is Concentrated Solar Power (CSP) technology integrated with thermal energy storage. The aim of this paper is to discuss the potential benefits related to the use of CSP technology by presenting innovative industrial solutions developed in the Italian SOLARGRID Project, namely the hybridization of CSP–PV systems and the solar thermo-electric system developed by MAGALDI, the parabolic trough collector of Eni, and the new linear Fresnel reflector configuration of IDEA S.r.l. These plant and component solutions are developed for improving the technical performance of CSP technology and reducing the levelized cost of electricity, thereby fostering an effective and massive deployment and encouraging the creation of new business models. [ABSTRACT FROM AUTHOR]

The increasing demand for energy-efficient and sustainable solutions in the building sector has driven the need for innovative approaches that integrate renewable energy sources and advanced control systems. This paper presents an integrated energy management solution for solar-powered smart buildings, combining a multifaceted physical system with advanced IoT- and cloud-based control systems. The physical system includes a heat pump, photovoltaics, solar thermal panels, and an innovative low-enthalpy radiant wall and ceiling, providing self-sufficient heating and cooling. The control system makes use of advanced IoT and communication engineering technologies, using Modbus, HTTP, and MQTT protocols for seamless interconnectivity, monitoring, and remote management. The successful implementation of this solution in an average-sized model house in Paris and a deep energy retrofit of a semidetached single-family house in Oviedo, northern Spain, demonstrates increased energy efficiency, improved thermal comfort, and reduced environmental impact compared with conventional alternatives. This study illustrates the potential of integrating solar energy, IoT, and communication technologies into smart buildings, contributing to the global effort to reduce the environmental impact of the building sector. [ABSTRACT FROM AUTHOR]

Precise mapping and continuous monitoring of fine-scale surface albedo are indispensable for assessing and optimizing renewable energy sources. Understanding the variations in surface reflectivity is crucial in capturing the full potential of renewable technologies, as it directly impacts the efficiency of harnessing solar energy for sustainable power generation. Satellite remote sensing stands out as the sole practical approach for estimating surface albedo at both regional and global scales. Although there are different methods to calculate albedo from satellite data, most satellite products result in low spatial resolution for large heterogeneous areas, such as urban and peri-urban areas. This paper evaluates and compares several methodologies to calculate surface albedo from Landsat 8 imagery. As a result, a new methodology for estimating surface albedo for heterogeneous areas has been proposed. The new methodology has been compared with direct and indirect albedo measurements, improving the original methodologies of Baldinelli and Silva with respect to the Arctic-Boreal Vulnerability Experiment (ABOVE) albedo images by reducing the RMSE by 85% and 52%, respectively. [ABSTRACT FROM AUTHOR]

Flexible DC power grids are widely recognized as an important component of building smart grids. Compared with traditional AC power grids, flexible DC power grids have strong technical advantages in islanding power supplies, distributed power supplies, regional power supplies, and AC system interconnection. In multi-terminal flexible DC power grids containing renewable energy sources such as solar and wind power, due to the instability and intermittency of renewable energy, it is usually necessary to add energy storage units to pre-regulate the power of the multi-terminal flexible DC power grid in islanded operation. Aiming at the important problem of large current impact and serious consequences when the flexible DC distribution network fails, a combined location method combining an improved impedance method (series current-limiting reactors at both ends of the line to obtain a more accurate current differential value) and a particle swarm optimization algorithm is proposed. Initially, by establishing the enhanced impedance model, the differential variables under the conditions of inter-electrode short-circuit and single-pole grounding fault can be obtained. Then tailor-made fitness functions are designed for these two models to optimize parameter identification. Subsequently, the iterative parameters of the particle swarm optimization algorithm are fine-tuned, giving it dynamic sociality and self-learning ability in the iterative process, which significantly improves the convergence speed and successfully avoids local optimization. Finally, various fault types in a six-terminal DC distribution network are simulated and analyzed by MATLAB, and the results show that this method has good accuracy and robustness. This research provides strong theoretical and methodological support for improving the safety and reliability of DC distribution systems. [ABSTRACT FROM AUTHOR]

This article describes an example of using the measurement data from photovoltaic systems and wind turbines to perform practical probabilistic calculations around green hydrogen generation. First, the power generated in one month by a ground-mounted photovoltaic system with a peak power of 3 MWp is described. Using the Metalog family of probability distributions, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is calculated. Identical calculations are performed for the simulation data, allowing us to determine the power produced by a wind turbine with a maximum power of 3.45 MW. After interpolating both time series of the power generated by the renewable energy sources to a common sampling time, they are summed. For the sum of the power produced by the photovoltaic system and the wind turbine, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is again calculated. The presented calculations allow us to determine, with probability distribution accuracy, the amount of hydrogen generated from the energy sources constituting a mix of photovoltaics and wind. The green hydrogen production model includes the hardware and the geographic context. It can be used to determine the preliminary assumptions related to the production of large amounts of green hydrogen in selected locations. The calculations presented in this article are a practical example of Business Intelligence. [ABSTRACT FROM AUTHOR]

This study examines Finland's increasing investment in solar energy as part of its broader strategy to transition to renewable energy sources. Despite its northern location and limited sunlight during winter months, Finland has effectively harnessed solar power, especially during its long summer days. We conducted a PESTLE analysis, highlighting political commitment to climate agreements, economic benefits of solar energy, public support for clean energy, technological advancements, regulatory support, and environmental benefits. In addition, we applied the autoregressive distributed lag model (ARDL) to study the impact of GDP per capita, urbanization (URB), foreign direct investments (FDI), and the share of primary energy consumption from solar (SOL) on C O 2 emissions for Finland during 1990–2022. The long-term findings show that SOL, URB, and FDI negatively impact C O 2 emissions, while GDP positively impacts C O 2 emissions. Solar energy deployment reflects the effectiveness of solar energy as a cleaner alternative to fossil fuels. Urbanization also shows a negative impact on C O 2 emissions due to better infrastructure and more efficient energy use in urban areas. FDI suggests that investments are oriented towards sustainable technologies. Industrial activity associated with economic expansion may indicate the positive effect of GDP in C O 2 emissions. Although economic growth has typically resulted in increased emissions, strategic investments in renewable energy and sustainable urban development can greatly reduce this effect. Policy initiatives in Finland should concentrate on promoting green investments and enhancing urban planning with a focus on environmental sustainability. [ABSTRACT FROM AUTHOR]

Nowadays, due to climate change and disappearance of fossil fuels, hybrid electric UAVs using renewable energy sources are being developed. In addition, although research on UAVs with a large wingspan and high weight is common due to their long endurance, research on mini UAVs has remained limited. This study aims to increase the energy capacity of solar-powered mini UAVs and thus extend their endurance by developing a fixed-wing hybrid UAV that can fly with solar energy as much as possible, especially during the cruise phase. In this study, a solar-powered mini VTOL (vertical take-off and landing) UAV with a wingspan of 1.8 m and weight of 3.3 kg is developed and a model of the system consisting of solar cells, a battery, a super capacitor, and a DC/DC converter is created in MATLAB/Simulink software (R2023b). Additionally, state machine control (SMC), a rule-based (RB) energy management strategy (EMS), has been applied to this model. While the power obtained from the sun is divided among the other energy components, the durability of the UAV is increased, and the excess energy is stored as altitude energy to be used when necessary. As a result, in this study, an energy management algorithm including altitude energy has been successfully applied to a solar-powered UAV, achieving an 11.11% energy saving. [ABSTRACT FROM AUTHOR]

It is very important to analyze and forecast energy production for investments in renewable energy resources. In this study, the energy production of wind and solar power plants, which are among the leading renewable energy sources, was estimated using deep learning. For a solar power plant, three different solar power plants with 1MW installed power were examined. Three-year energy production data of power plants were taken. These data were used with the deep learning method long short-term memory (LSTM) and seasonal autoregressive moving average (SARIMA). Results were obtained for each dataset; they were subjected to five different (MSE, RMSE, NMSE, MAE, and MAPE) error performance measurement systems. In the LSTM model, the highest accuracy rate was 81% and the lowest accuracy rate was 59%. In the SARIMA model, the highest accuracy rate was 66% and the lowest accuracy rate was 41%. As for wind energy, wind speeds in two different places were estimated. Wind speed data were taken from meteorological stations. Datasets were tested with MAPE, R2, and RMSE error performance measurement systems. LSTM, GRU, CNN-LSTM, CNN-RNN, LSTM-GRU, and CNN-GRU deep learning methods were used in this study. The CNN-GRU model achieved a maximum accuracy of 99.81% in wind energy forecasting. [ABSTRACT FROM AUTHOR]

Solar energy, as a clean and renewable energy source, holds significant promise for addressing water shortages. Utilizing solar energy for water evaporation is seen as an effective solution in this regard. While many existing interfacial photothermal water evaporation systems rely on nanoparticles or graphene as photothermal or support materials, this study introduced polydopamine (PDA) as a photothermal material due to its environmental friendliness and excellent photon absorption characteristics that closely match the solar spectrum. Polystyrene (PS) was also introduced as a support material for its porous structure and density similar to water, enabling it to float on water. The resulting PS-PDA composite porous structure solar evaporator exhibited a photothermal conversion efficiency comparable to nanoparticles (over 75%), yet with lower production costs and minimal environmental impact. This innovative approach offers a scalable solution for water-scarce regions, providing a cost-effective and efficient means to address water scarcity. The use of PDA and PS in this context highlights the potential for utilizing common materials in novel ways to meet pressing environmental challenges. [ABSTRACT FROM AUTHOR]

Generating hydrogen by electrolysis in an alkaline system with a green power source consisting of wind turbines (WTs) and photovoltaic (PV) power is a promising and sustainable way to produce clean hydrogen to reduce greenhouse gas emissions. This study utilized TRNSYS 16 software to perform a dynamic simulation of a hydrogen system. TRNSYS, which stands for Transient System Simulation Program, is a software package designed for simulating the dynamic behaviour of thermal and electrical energy systems. It is widely used to analyze and optimize the performance of various energy systems. This system incorporated a PV power source and a WT for electricity generation, along with an electrolyzer for hydrogen production. The analysis was carried out to evaluate variable weather conditions, specifically wind speed, solar radiation, and temperature. These factors have a direct impact on the system's performance, influencing the available power as a consequential outcome. The results reveal that, given the specific climate conditions in the Markham zone, Toronto, the integrated renewable system is capable of consistently providing electricity and meeting the load demand throughout the entire year. However, it is noteworthy that on cold days when solar radiation is limited, the WT emerges as the most effective and efficient power source. The analysis also indicates that the system reliably supplies enough energy to meet the laboratory's load demand. Moreover, the system's performance is particularly impressive with the WT as the power source, as it can generate a maximum of 9.03 kg of hydrogen per month. In contrast, the PV power source yields only 0.58 kg H2. Additionally, the cost per kilogram of hydrogen (kg H2) is considerably lower when the WT is used, at USD 0.55/kg H2, while it rises to USD 1.5/kg H2 when PV is the power source. These findings underscore the significance of using the most suitable power source, such as a WT, in specific climatic conditions and regions in terms of both performance and cost-effectiveness. [ABSTRACT FROM AUTHOR]

The development of renewable energy in agricultural landscapes has led to new debates about siting solar, wind, and other energy projects. Concerns for protecting food production and prime agricultural soils are often leading points of resistance to renewable energy projects. This resistance has grown, even as the urgency of transitioning away from fossil fuels has increased. The economic stakes are high, particularly for farmers seeking to diversify and stabilize farm income with renewables, but few studies have examined the likely magnitude of effects, either on food production or on farm incomes, implied by expanding renewables. How extensively are hosting communities likely to be impacted, and what do farmers stand to gain, or lose, in these debates? Focusing on a portion of New York State (NYS), with its aggressive solar development goals, we evaluated the effects of state solar targets on farmland and the economic potential for farmers leasing land. In comparison to current income from leading crops, land leasing alone would imply an increase of $42 million per year in local revenue, while affecting less than 12 percent of non-food producing, non-prime soils within the study area. The areal impacts are larger in our imaginations than in the real landscapes, and the debate has far-reaching implications for policy beyond farming areas. [ABSTRACT FROM AUTHOR]

This article reflects on the necessity of employing renewable energy sources in the modern era to mitigate the negative environmental impact caused by traditional energy sources and address environmental pollution. Through research conducted in Azerbaijan and Hungary, it analyses the influence of economic growth on the ecological environment and renewable energy production. Due to limitations in the general dataset, the study considers the period of 1997–2022 for CO2 emissions causing environmental pollution, 2007–2022 for renewable energy production in Azerbaijan, and 2000–2021 for the same in Hungary. Information regarding wind and solar energy in Azerbaijan has been available since 2013. Temporal sequences have been utilised in the research, employing Augmented Dickey–Fuller and Phillips–Perron (PP) unit root tests to examine the stationarity of the time series. An Autoregressive Distributed Lag (ARDL) model has been constructed, and the credibility of the model has been verified using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) models. The findings reveal that in Azerbaijan, the long-term impact of economic growth on hydro-energy has been negative, while dependence on biomass and waste has been insignificant but positive. The influence on wind and solar energy production has also been negative and insignificant, akin to hydro-energy production. However, energy supply from renewable sources has been positively affected by the aggregate indicator of economic growth, albeit insignificantly. The impact of economic growth on carbon dioxide has been significant in two magnitudes, whereas in other cases, it has been insignificant but positive. In Hungary, economic growth has positively affected renewable energy production. However, the impact on carbon dioxide has been negative, meaning that this indicator has decreased as economic growth has increased. The study concludes that the impact of economic growth on indicators of both countries has been more effective in Hungary, which can be attributed to economic development. [ABSTRACT FROM AUTHOR]

The growing urgency for sustainable energy solutions necessitates a deeper understanding of the environmental impacts of renewable technologies. This article aims to synthesize and analyze Life Cycle Assessments (LCA) in this domain, providing a comprehensive perspective. We systematically categorized 2923 articles into four sectors: (1) photovoltaic systems, (2) wind energy systems, (3) solar thermal systems, and (4) materials for auxiliary industry supporting these systems. A comparative analysis was conducted to identify methodological consistencies and disparities across these sectors. The findings reveal diverse methodological approaches and a range of environmental impacts, highlighting the complexities in assessing renewable energy systems. The article underscores the significance of material selection in photovoltaic, solar, and wind systems, providing a critical overview of the current state of LCA research in renewable energy and stressing the need for standardized methodologies. It also identifies gaps in recent research, offering insights for future studies focused on integrating environmental, economic, and social considerations in renewable energy assessments. Integrating environmental assessments provides a robust framework for making informed decisions on sustainable technologies. The findings are critical for projects that balance technological needs with sustainability goals. [ABSTRACT FROM AUTHOR]

This work provides an in-depth investigation of the dynamic interaction patterns between water stocks and renewable energy markets through the application of continuous wavelet analysis, dynamic correlation analysis, and time-varying Granger causality analysis. In addition, this study utilizes daily pricing indices, namely the S&P Global Water Index, Solactive Global Wind Energy Index, and Solactive Global Solar Energy Index, spanning from 18 May 2011 to 23 June 2022. The results show significant correlation patterns between the indices, ranging from moderate to high. Notably, robust correlations have been detected starting from 2015. The research also discovered a varied and inconsistent relationship between frequency and causation throughout different time periods. Moreover, the results reveal an asymmetry in the causal effects and a symmetry correlation at tail quantile ranges. Policymakers and market participants must consider these insights to make wise financial and strategic decisions. [ABSTRACT FROM AUTHOR]

The increase in demand for electricity due to industrial development and the growing use of electronic equipment has led to the need for new alternative energy sources, such as photovoltaic solar energy conversion. In this context, organic photovoltaic (OPV) stands out as an efficient way of generating energy indoors. An electrical power supply system (EPS) was developed to power a commercial electronic lock, using OPV as the energy source. The system was designed with a charging control to match the panel voltage to a supercapacitor charge accumulator and a boost converter. Tests on the bench and in a real scenario demonstrated the viability and effectiveness of the system, showing that it is capable of operating self-sufficiently with a minimum daily illuminance of 250 lux for 12 h. The results indicate that the system can be applied to various loads with low energy consumption, promoting energy efficiency in different contexts. [ABSTRACT FROM AUTHOR]

Federal and state governments in the United States (US) are promoting the transition from traditional Diesel School Buses to Electric School Buses (ESBs). This would prevent the emission of deleterious air pollutants that affect students and communities while simultaneously contributing to a reduction in greenhouse gases, aiding in the fight against climate change. However, due to their significant size and long routes, ESBs require large batteries with significant electricity demand. If this additional electricity demand is supplied to hundreds of thousands of EBSs at peak consumption times, the strain on the grid may be detrimental, while transportation costs for schools could dramatically increase. Furthermore, if EBSs are charged using traditional hydrocarbon generation, the environmental benefits of these projects may be significantly reduced. Therefore, applying renewable energy presents a host of synergistic opportunities to reduce emissions while providing inexpensive electricity to schools. Solar energy is abundant in large portions of the US, potentially providing many schools with ample inexpensive and sustainable electricity to power their transportation equipment and meet other requirements at their facilities. This research developed a novel framework to integrate publicly available big data provided by federal and state agencies in the US, as well as National Laboratories, to provide stakeholders with actionable information to develop EBS grid-to-vehicle (V2G) systems across the US. Geographic Information Systems, data analytics and Business Intelligence were applied to assess and characterize solar energy generation and consumption patterns. The novel integration of the systems in the proposed framework provided encouraging results that have practical implications for stakeholders to develop successful and sustainable ESB V2G facilities. These results identified many schools across the US that would significantly benefit from the use of solar energy and be able to supply their local communities during idle times with renewable energy through V2G. The renewable energy resource would be capable of charging ESBs at a low cost for operational availability as required. The results indicate that the proposed ESB V2G system will provide significant benefits to both schools and their local communities. The feasibility of the proposed endeavor was validated by the results of the study, providing both school and solar energy stakeholders with insights into how to better manage such a complex system. [ABSTRACT FROM AUTHOR]

Renewable energy can lead to a sustainable future and solar energy is one the primary sources of renewable energy. Solar energy is harvested mainly by photovoltaic plants. Though there are a large number of solar panels, the economic efficiency of solar panels is not that high in comparison to energy production from coal or nuclear matter. The main risk involved in solar plants is the high maintenance cost involved in maintaining the plants. To help reduce this issue, automated solutions using Unmanned Aerial Vehicles (UAVs) and satellite imagery are proposed. In this research work, we propose a novel deep learning architecture for the segmentation of solar plant aerial images, which not only helps in automated solar plant maintenance, but can also be used for the area estimation and extraction of solar panels from an image. Along with this, we also propose a transfer learning-based model for the efficient classification of solar panel damage. Solar panel damage classification has a lot of applications. It can be integrated into monitoring systems, raising alerts when there is severe damage or damage of a certain type. The adaptive UNet model with Atrous Spatial Pyramid Pooling (ASPP) module that performed the dilated convolutions that we proposed achieved an overall accuracy of 98% with a Mean Intersection-Over-Union (IoU) Score of 95% and took under a second to process an image. Our classification model using Visual Geometry Group 19 (VGG19) as the backbone for feature extraction has achieved a classification accuracy of 98% with an F1 score of 99%, thus detecting the five classes of damage, including undamaged solar panels, in an efficient manner. [ABSTRACT FROM AUTHOR]

Geographically distributed cloud data centers (DCs) consume enormous amounts of energy to meet the ever-increasing processing and storage demands of users. The brown energy generated using fossil fuels is expensive and significantly contributes to global warming. Considering the environmental impact caused by the high carbon emissions and relatively high energy cost of brown energy, we propose the integration of renewable energy sources (RES), especially solar and wind energy, with brown energy to power cloud data centers. In our earlier study, we addressed the intermittency of renewable energy sources, where we replaced the random initialization of artificial neural network (ANN) edge weights with the harmony search algorithm (HSA)-optimized assignment of weights. This study incorporated reliably forecast solar and wind energy into the input parameters of our proposed green energy manager (GEM), for cost minimization, carbon emission minimization, and better energy management of cloud DCs, to make our current study more reliable and trustworthy. Four power sources, on-site solar energy and wind energy, off-site solar energy and wind energy, energy stored in energy storage devices, and brown energy, were considered in this study and simulations were carried out for three different cases. The simulation results showed that case 1 (all brown) was 58% more expensive and caused 71% higher carbon emissions than case 2.1 (cost minimization). Case 1 (all brown) was 39% more expensive and had 80% higher carbon emissions than case 2.2 (carbon emission minimization). The simulation results justify the necessity and importance of the GEM, and finally the results proved that our proposed GEM is less expensive and more environmentally friendly. [ABSTRACT FROM AUTHOR]

Despite the global importance of solar energy, its variability requires energy storage to balance production during peak and off-peak periods. Moreover, the transport sector is undergoing a global transition from internal combustion engines to electric vehicles. Since vehicles are idle 95% of the time, electric vehicle batteries, when connected to a grid, can effectively regulate intermittent photovoltaics using vehicle-to-grid technology. This conceptual study investigates the feasibility of a nationwide energy infrastructure that relies solely on solar energy, replacing other electricity sources, such as solid fuels, petroleum products, and natural gas, and utilizes electric vehicles as the sole battery energy storage system. This study aims to demonstrate the significant potential and benefits of such collaboration. The theoretical study combines historical data, assumptions, and conditions to build a simulation model that is modelled similarly as in previous conceptual studies of nationwide energy systems based solely on photovoltaics and electric vehicles, referenced in this article. In Slovenia, the total surface size suitable for the installation of photovoltaic systems is estimated to be 280 km2. The calculations show that a surface size of 217 km2 for photovoltaic systems can produce enough energy to cover Slovenia's entire energy demand, Slovenia's final energy consumption. However, simulations comparing photovoltaic production, total energy consumption (electricity, solid fuels, etc.), and the capacity of electric vehicle batteries show that a surface size of more than 500 km2 with photovoltaic systems and a 200% share of electric vehicles in the Slovenian vehicle fleet in 2022 will provide satisfactory results. Therefore, for a country like Slovenia, in addition to a solar power plant with a surface size of 280 km2, additional renewable energy sources are needed to cover the total energy demand, as well as additional battery energy storage systems in addition to electric vehicles. [ABSTRACT FROM AUTHOR]

A novel energy management system featuring a unique framework involving multiple hierarchical controllers at the distribution and transmission network levels is proposed. The unique objective function of this energy management system is designed to enhance system inertia during black start and optimise load shedding. The objective function further aims to increase reliance on renewable energy sources, prioritising solar power along with battery and fuel cell technologies. This work delves deeply into the dynamics of multi-area power networks, where some areas possess black start capabilities (BSAs) while others do not (NBSAs). The proposed energy management system specifically explores the complex interplay between these black start capabilities and the hierarchical load restoration order. During grid blackouts, the systems located in BSA areas are tasked with first restoring essential loads in their own regions before extending aid to the adjacent NBSA areas, taking into account factors such as their available reserved power and geographical proximity. This work is extended to analyse complex multi-area power network architectures. This extended analysis provides invaluable insights for enhancing power restoration processes and facilitating the large-scale integration of sustainable energy solutions in complex systems. The proposed energy management system is validated using the IEEE 39-Bus network, which consists of ten distinct areas, each differing in their black start capabilities. The results demonstrate the superiority of the proposed system. [ABSTRACT FROM AUTHOR]