Search Results (2,178)

This paper addresses the critical challenge of energy management for autonomous robots in the context of large-scale photovoltaic parks. The dynamic and vast nature of these environments, characterized by dense, structured rows of solar panels, introduces unique complexities, including uneven terrain, varied operational demands, and the need for equitable resource allocation among diverse robot fleets. The presented framework adapts and significantly extends the Affinity Propagation algorithm for strategic charging station placement within photovoltaic parks. The key contributions include: (1) a multi-attribute grid-based environment model that quantifies terrain difficulty and panel-specific obstacles; (2) an extended multi-factor scoring function that incorporates penalties for terrain inaccessibility and proximity to sensitive photovoltaic infrastructure; (3) a sophisticated, energy-aware consumption model that accounts for terrain friction, slope, and rolling resistance; and (4) a novel multi-agent fairness constraint that ensures equitable access to charging resources across heterogeneous robot sub-fleets. Through extensive simulations on synthesized photovoltaic park environments, it is demonstrated that the enhanced algorithm not only significantly reduces travel distance and energy consumption but also promotes a fairer, more efficient operational ecosystem, paving the way for scalable and sustainable robotic maintenance and inspection. Full article

Seawater desalination has emerged as a crucial solution for addressing global freshwater scarcity. However, it generates significant volumes of concentrated brine waste. This brine is rich in dissolved salts and minerals, primarily, chloride (55%), sodium (30%), sulfate (8%), magnesium (4%), calcium (1%), potassium (1%), bicarbonate (0.4%), and bromide (0.2%), which are often discharged into marine environments, posing ecological challenges. This study presents a comprehensive global review of innovative technologies for recovering these constituents as valuable products, thereby enhancing the sustainability and economic viability of desalination. The paper evaluates a range of proven and emerging recovery methods, including membrane separation, nanofiltration, electrodialysis, thermal crystallization, solar evaporation, chemical precipitation, and electrochemical extraction. Each technique is analyzed for its effectiveness in isolating salts (NaCl, KCl, and CaSO₄) and minerals (Mg(OH)₂ and Br₂), with a discussion of process-specific constraints, recovery efficiencies, and product purities. Furthermore, the study incorporates a detailed techno-economic assessment, highlighting revenue potential, capital and operational expenditures, and breakeven timelines. Simulated case studies of a 100,000 m³/day seawater reverse osmosis (SWRO) facility demonstrates that a sequential brine recovery process and associated energy balances, supported by pilot-scale data from ongoing global initiatives, can achieve over 90% total salt recovery while producing marketable products such as NaCl, Mg(OH)₂, and Br₂. The estimated revenue from recovered materials ranges between USD 4.5 and 6.8 million per year, offsetting 65–90% of annual desalination operating costs. The analysis indicates a payback period of 3–5 years, depending on recovery efficiency and product pricing, underscoring the economic viability of large-scale brine valorization alongside its environmental benefits. By transforming waste brine into a source of commercial commodities, desalination facilities can move toward circular economy models and achieve greater sustainability. A practical integration framework is proposed for both new and existing SWRO plants, with a focus on aligning with the principles of a circular economy. By transforming waste brine into a resource stream for commercial products, desalination facilities can reduce environmental discharge and generate additional revenue. The study concludes with actionable recommendations and insights to guide policymakers, engineers, and investors in advancing brine mining toward full-scale implementation. Full article

A hybrid energy system (HES) integrates various energy resources to attain synchronized energy output. However, HES faces significant challenges due to rising energy consumption, the expenses of using multiple sources, increased emissions due to non-renewable energy resources, etc. This study aims to develop an energy management strategy for distribution grids (DGs) by incorporating a hydrogen storage system (HSS) and demand-side management strategy (DSM), through the design of a multi-objective optimization technique. The primary focus is on optimizing operational costs and reducing pollution. These are approached as minimization problems, while also addressing the challenge of achieving a high penetration of renewable energy resources, framed as a maximization problem. The third objective function is introduced through the implementation of the demand-side management strategy, aiming to minimize the energy gap between initial demand and consumption. This DSM strategy is designed around consumers with three types of loads: sheddable loads, non-sheddable loads, and shiftable loads. To establish a bidirectional communication link between the grid and consumers by utilizing a distribution grid operator (DGO). Additionally, the uncertain behavior of wind, solar, and demand is modeled using probability distribution functions: Weibull for wind, PDF beta for solar, and Gaussian PDF for demand. To tackle this tri-objective optimization problem, this work proposes a hybrid approach that combines well-known techniques, namely, the non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (Hybrid-NSGA-II-MOPSO). Simulation results demonstrate the effectiveness of the proposed model in optimizing the tri-objective problem while considering various constraints. Full article

Sugarcane, a vital economic crop and renewable energy source, requires precise monitoring of the area in which it has been planted to ensure sugar industry security, optimize agricultural resource allocation, and allow the assessment of ecological benefits. Guangxi Zhuang Autonomous Region, leveraging its subtropical climate and abundant solar thermal resources, accounts for over 63% of China’s total sugarcane cultivation area. In this study, we constructed an enhanced RCAU-net model and developed a refined extraction framework that considers different growth stages to enable rapid identification of sugarcane planting areas. This study addresses key challenges in remote-sensing-based sugarcane extraction, namely, the difficulty of distinguishing spectrally similar objects, significant background interference, and insufficient multi-scale feature fusion. To significantly enhance the accuracy and robustness of sugarcane identification, an improved RCAU-net model based on the U-net architecture was designed. The model incorporates three key improvements: it replaces the original encoder with ResNet50 residual modules to enhance discrimination of similar crops; it integrates a Convolutional Block Attention Module (CBAM) to focus on critical features and effectively suppress background interference; and it employs an Atrous Spatial Pyramid Pooling (ASPP) module to bridge the encoder and decoder, thereby optimizing the extraction of multi-scale contextual information. A refined extraction framework that accounts for different growth stages was ultimately constructed to achieve rapid identification of sugarcane planting areas in Guangxi. The experimental results demonstrate that the RCAU-net model performed excellently, achieving an Overall Accuracy (OA) of 97.19%, a Mean Intersection over Union (mIoU) of 94.47%, a Precision of 97.31%, and an F1 Score of 97.16%. These results represent significant improvements of 7.20, 10.02, 6.82, and 7.28 percentage points in OA, mIoU, Precision, and F1 Score, respectively, relative to the original U-net. The model also achieved a Kappa coefficient of 0.9419 and a Recall rate of 96.99%. The incorporation of residual structures significantly reduced the misclassification of similar crops, while the CBAM and ASPP modules minimized holes within large continuous patches and false extractions of small patches, resulting in smoother boundaries for the extracted areas. This work provides reliable data support for the accurate calculation of sugarcane planting area and greatly enhances the decision-making value of remote sensing monitoring in modern agricultural management of sugarcane. Full article

Vertical-Axis Wind Turbines (VAWTs) are efficient solutions for renewable energy generation, especially in regions with variable wind conditions. This study presents an optimized design of a small-scale H-type VAWT through the integration of Design of Experiments (DOE) and Computational Fluid Dynamics (CFD), using a fractional factorial 2^k−p approach to evaluate the influence of geometric and operational parameters on power output and power coefficient (Cp), which ranged from 0.15 to 0.35. The research began with a comprehensive assessment of renewable resources in Isla Fuerte, Colombia. Solar analysis revealed an average of 5.13 Peak Sun Hours (PSHs), supporting the existing 175 kWp photovoltaic system. Wind modeling, based on meteorological data and Weibull distribution, showed speeds between 2.79 m/s and 5.36 m/s, predominantly from northeast to northwest. Under these conditions, the NACA S1046 airfoil was selected for its aerodynamic suitability. The turbine achieved power outputs from 0.46 W to 37.59 W, with stabilization times analyzed to assess dynamic performance. This initiative promotes environmental sustainability by reducing reliance on Diesel Generators (DGs) and empowering local communities through participatory design and technical training. The DOE-CFD methodology offers a replicable model for energy transition in insular regions of developing countries, linking technical innovation with social development and education. Full article

A microgrid (MG) topology combines various kinds of resources like solar photovoltaic (PV) systems, wind turbines (WTs), energy storage systems, and the conventional utility grid. These different resources need to be coordinated in an optimal way to keep the power balanced, reduce the operational cost, and make the system resilient to any kind of failures. Therefore, an efficient energy management system (EMS) is essential in an MG system to provide suitable and reliable operation under different weather and demand load conditions. In this paper, a new EMS-based multi-objective Nizar Optimization Algorithm (NOA) is proposed. The suggested EMS aims to improve the power quality problem caused by the unpredictable nature of renewable energy sources and then minimize the grid power and battery degradation costs. By leveraging the adaptability of the NOA, the applied EMS method simply optimizes the allocation and energy sharing of the resources in a grid-connected MG. The proposed EMS was verified in simulation using MATLAB software. The performance of the proposed EMS was tested under different weather conditions, and the obtained results have been compared with those obtained in the existing methods. The obtained results indicate that the proposed EMS based on the NOA is capable of adjusting the multi-source energy allocation with minimal grid costs and the battery degradation issue. The proposed NOA indicates robust performance with total cost savings varying from USD 17 to USD 34 compared to other optimizers, as well as a great reduction in degradation cost, up to 27% improvement over the conventional methods. Finally, the proposed EMS offers several advantages over the conventional methods, including the improved dynamic system, faster convergence, lower operational costs, and higher energy efficiency. Full article

Concentrated solar power technology offers an effective pathway for large-scale renewable electricity generation in areas with abundant solar resources. This study develops and evaluates a cascaded multigeneration system that integrates a solar power tower with a subcritical-CO₂ Brayton cycle, a bottoming Rankine cycle, and a multi-effect distillation unit designed for Mediterranean conditions. The system achieves a maximum net power output of 23.48 MW and a freshwater production rate of 14.25 kg/s during peak summer conditions. The analysis reveals that high solar availability enables CO₂ mitigation of up to 27,434.55 kg-CO₂/h, thereby emphasizing the strong environmental benefits of the proposed system. The integrated Sb-CO₂–Rankine–Desalination configuration attains overall thermal efficiencies of 0.39–0.43, while exergy efficiencies reach 0.58 and 0.73 for the Sb-CO2 and Rankine subsystems, respectively. Sensitivity analysis further confirms the strong pressure dependence of the Sb-CO₂ cycle, with an optimum operation condition around 10 bar and 1100 °C, whereas the Rankine cycle exhibits a steady efficiency enhancement with increasing pressure. The proposed system achieves a simple payback period of 15.3 years, demonstrating its economic feasibility under Mediterranean climatic conditions. Full article

The global energy transition is expected to require three to twenty times more land than fossil fuel-based power generation, making the availability of suitable land for the global energy transition a key challenge. Based on different types of energy resources, this study designs a telecoupling multi-regional input–output (MRIO) model to analyze cross-border electricity-driven embodied land appropriation patterns. The results show that the land footprint associated with renewable energy is substantially lower than that associated with conventional power generation. However, the growth rate of this footprint is 2.18 times higher than that of conventional electricity generation. China and Germany are identified as key export markets for wind- and solar- driven embodied land. The share of electricity-driven embodied land from China to the United States, Japan, and Germany declined, whereas the embodied land flowing to countries including South Korea, India, and Singapore increased. Embodied land-exporting nations face trilemma issues related to environmental degradation chain reactions, resource consumption threshold lines, and social distribution tensions, which may significantly affect decarbonization progresses. By integrating renewable power infrastructures and land use occupation, this analytical framework is expected to advance the understanding of energy–land nexus dynamics, providing theoretical foundations for cross-system governance in the implementation of carbon neutrality. Full article

The hybridization of photovoltaic (PV) and concentrated solar power (CSP) technologies offers a viable solution to enhance dispatchability and reduce energy costs in solar power systems. This study analyzes two CSP-PV hybrid configurations—parabolic trough and solar tower—in diverse Brazilian climatic conditions. Particular focus is given to Bom Jesus da Lapa, identified as the most favorable location in terms of solar resource and system performance. The CSP subsystem includes a two-tank direct thermal energy storage system with molten nitrate salts and a 50 MWe gross Rankine cycle. System performance and techno-economic metrics are assessed using the System Advisor Model (SAM). A parametric analysis investigates the impact of solar irradiation, solar multiple (SM), and thermal storage duration on annual energy output and levelized cost of energy (LCOE). Results indicate that the hybrid system consistently surpasses standalone PV and CSP in both performance and cost-effectiveness. In the solar tower configuration, capacity factors reach up to 90% with an SM of 3.5 and 12 h of storage. This work provides the first techno-economic assessment of PV/CSP hybrid plants tailored to Brazilian conditions, combining multi-city simulations with solar multiple and storage parametric analysis. Among all evaluated sites, Bom Jesus da Lapa presents the highest energy yield and lowest LCOE, supporting its potential suitability for hybrid CSP-PV deployment. Full article

Among the Central Asian republics, Uzbekistan is unique in that approximately 80% of its territory lies within a plain, characterized by an arid geographic zone and dry climate. Agricultural production in these regions is possible only through artificial irrigation. In recent years, global climate change and challenges related to transboundary water use have led to a reduction in water availability. The average annual water allocation to Uzbekistan is estimated at 51–53 billion m³, of which 90–91% is consumed by the agricultural sector. Due to the uneven distribution of water resources and the complex topography of irrigated lands, water supply is supported by numerous pumping stations operated by the state, water users associations, farms, and clusters. Additionally, well-based pumping systems are employed to maintain groundwater levels and ensure irrigation. On average, these facilities consume around 8.0 billion kWh of electricity annually. The agricultural sector faces several critical challenges, including crop water deficits caused by water shortages, slow adoption of water-saving technologies, and limited implementation of drip irrigation on household plots, dachas, and greenhouses that play a key role in food supply. Moreover, the delivery of water to fertile lands situated far from main power lines and water sources remains problematic. This article aims to explore the integration of solar energy solutions to support drip irrigation in both large-scale agricultural lands (ω = 1.0–100.0 ha and above) and small-scale areas such as homestead plots, dachas, and greenhouses (ω = 0.01–1.0 ha), as well as their application in small- to medium-sized pumping stations. Based on the research and experimental design work carried out, three mobile photovoltaic units—MPPU-8-500-4000, MPPU-2-550-1100, and MPPU-4-500-2000—were developed and implemented to address water and energy shortages in agriculture. Full article

This study presents a novel LSTM–CNN-based adaptive scheduling framework (LSTM-CNN–AS) designed to improve real-time energy management and extend the lifespan of lithium-ion Battery Energy Storage Systems (BESS) in rural and resource-constrained microgrids. In contrast to conventional methods that prioritize economic optimization, the proposed framework incorporates state of health (SOH) aware control and adaptive closed-loop scheduling to enhance operational reliability and battery longevity. The architecture combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for accurate SOH estimation, with lightweight Multi-Layer Perceptron (MLP) models supporting real-time scheduling and state of charge (SOC) regulation. Operational safety is maintained by keeping SOC within 20–80% and SOH above 70%. The proposed model training and validation are conducted using two real-world datasets: the Mendeley Lithium-Ion SOH Test Dataset and the DKA Solar System Dataset from Alice Springs, both sampled at 5-min intervals. Performance is evaluated across three operational scenarios, which are 2C charging with random discharge; random charging with 3C discharge; and fully random profiles, achieving up to 44% reduction in MAE and an R²; score of 0.9767. A one-month deployment demonstrates a 30% reduction in charging time and 40% lower operational costs, confirming the framework’s effectiveness and scalability for rural microgrid applications. Full article

Reliable water access in remote and desert-like regions remains a challenge, particularly in areas with limited infrastructure. Solar-powered submersible pumps offer a promising solution; however, optimizing their performance under variable environmental conditions remains a challenging task. This research presents an Artificial Intelligence (AI)-driven digital twin framework for modeling and optimizing the performance of a solar-powered submersible pump system. The proposed system has three core components: (1) an AI model for predicting the inverter motor’s output frequency based on the current generated by the solar panels, (2) a predictive model for estimating the pump’s generated power based on the inverter motor’s output, and (3) a mathematical formulation for determining the volume of water lifted based on the system’s operational parameters. Moreover, a dataset comprising 6 months of environmental and system performance data was collected and utilized to train and evaluate multiple predictive models. Unlike previous works, this research integrates real-world data with a multi-phase AI modeling pipeline for real-time water output estimation. Performance assessments indicate that the Random Forest (RF) model outperformed alternative approaches, achieving the lowest error rates with a Mean Absolute Error (MAE) of 1.00 Hz for output frequency prediction and 1.39 kW for pump output power prediction. The framework successfully estimated annual water delivery of 166,132.77 m³, with peak monthly output of 18,276.96 m³ in July and minimum of 9784.20 m³ in January demonstrating practical applicability for agricultural water management planning in arid regions. Full article

Water scarcity is a relevant issue whose impact can be mitigated through sustainable solutions. Humidification–dehumidification (HDH) cycles powered by photovoltaic thermal (PVT) modules enable pure water production in remote areas. In this study, models have been developed and validated for the main components of the system, the humidifier and the dehumidifier. A unique HDH-PVT prototype was built and experimentally tested at the SolarTech Lab of Politecnico di Milano in Milan, Italy. The experimental system is a Closed Air Closed Water—Water Heated (CACW-WH) that mimics a Closed Air Open Water—Water Heated (CAOW-WH) cycle through brine cooling, pure water mixing, and recirculation, avoiding a continuous waste of water. Tests were performed varying the mass flow ratio (MR) between 0.346 and 2.03 during summer and autumn in 2023 and 2024. The experimental results enabled the verification of the developed models. The optimal system performance was obtained for an MR close to 1 and a maximum cycle temperature of $44 °\mathrm{C}$, enabling a 0.51 gain output ratio (GOR) and 0.72% recovery ratio (RR). The electrical and thermal energy generation of the PVT modules satisfied the whole consumption of the system enabling pure water production exploiting only the solar resource available. The PVT-HDH system proved the viability of the proposed solution for a sustainable self-sufficient desalination system in remote areas, thus successfully addressing water scarcity issues exploiting a renewable energy source. Full article

Renewable energy sources offer a promising future, backed by mature technologies and a viable pathway toward sustainable energy systems. However, careful planning is necessary to efficiently utilize these resources, especially during site selection. Many rural areas lack access to grid electricity, making off-grid hybrid wind-solar power an attractive solution. In the Tigray region of Ethiopia, no such research has been conducted before. This study aims to identify suitable sites for hybrid wind-solar power for rural electrification using Geographic Information System (GIS), Analytic Hierarchy Process, and Monte Carlo simulation. The criteria fall into three categories: Climate, Topography, and Infrastructure, prioritized through pairwise comparisons by thirteen experts from five organizations engaged in renewable energy research, planning, and operations. Monte Carlo simulation was used for sensitivity analysis to address uncertainties in expert judgments and validate the rankings. The spatial analysis reveals 6470 km² as highly suitable for off-grid solar, 76 km² for off-grid wind with predominant easterly winds, and 177 km² as most favorable for hybrid generation. Areas of good suitability measure 447 km² for wind, 44,128 km² for solar, and 16,695 km² for hybrid systems. Based on this assessment, techno-economic analysis quantified the Levelized Cost of Energy (LCOE) under varying solar–wind shares and battery autonomy days. The analysis shows a minimum LCOE of $0.23/kWh with one-day storage and $0.58/kWh with three-day storage, indicating shorter autonomy is more cost-effective while longer autonomy enhances reliability. Sensitivity analysis shows financial parameters, particularly discount rate and battery capital cost, dominate system economics. Full article

The Moroccan Ryad, a cherished architectural treasure in North Africa, symbolizes cultural richness and architectural legacy. This paper advocates an inventive strategy to preserve and rejuvenate the Ryad as an energy-efficient model, intertwining modern sustainability principles. Employing a multidisciplinary approach, this study delves into the fusion of traditional Ryad design with contemporary sustainability, tackling energy efficiency, resource conservation, and cultural heritage challenges. Examining historical construction methods, material choices, and spatial arrangements alongside sustainable building practices, this paper showcases case studies of newly constructed sustainable Ryads. These examples spotlight strategies like passive solar design, renewable energy incorporation, water conservation, and local material use, augmenting energy performance while fostering cultural continuity through eco-friendly materials and traditional craftsmanship. This research also explores the social and economic impacts of this sustainable Ryad model, including its potential to boost tourism, generate employment, and engage communities. Additionally, it addresses the role of public policy in supporting sustainable practices for preserving Moroccan cultural heritage. By amalgamating culture, sustainability, and energy efficiency, this paper envisions a new role for the Moroccan Ryad, positioning it not only as a guardian of heritage but also as a symbol of environmentally responsible architecture—a model applicable to culturally rich regions globally. Full article