Search Results (83)

Dust accumulation on photovoltaic (PV) module surfaces significantly reduces energy yield in arid environments, yet traditional water-based cleaning techniques are unsustainable. This study presents a complete evaluation of electrodynamic screen (EDS) technology as a waterless cleaning solution, with a focus on dust removal efficiency and the critical trade-offs between energy gains and losses. Using printed circuit board (PCB) technology, a three-phase EDS system was designed and constructed, and its performance was simulated on COMSOL Multiphysics. Under optimal conditions of 50 Hz and a 66% duty cycle, the system achieves 52% dust removal efficiency while consuming less than one watt-hour per square meter during operation. The novelty of this study lies not merely in the hardware, but in the analytical lens applied and the holistic loss–gain methodology that quantitatively assesses, over a seven-day cycle, the reduction in dust-induced optical loss, the intrinsic optical transmission loss introduced by the EDS layer itself, and the operational energy cost of running the system. Rather than focusing on removal efficiency in isolation, this study enables a site-specific evaluation of whether, and when, EDS technology delivers a net energy benefit. By integrating material properties, local soiling conditions, and all major loss mechanisms, this study provides a low-cost and analytical tool for researchers and solar farm operators to assess the real-world feasibility of EDS cleaning before deployment. While the proposed PCB technology is not itself suitable for field deployment, it serves as a low-cost development platform for optimizing electrode geometry and excitation parameters prior to investment in transparent materials. Full article

Highland pastures in Montenegro, known as katuns, are seasonal settlements without grid power or network coverage and which are located where conventional monitoring is unfeasible. This study presents a solar-powered, off-grid system for livestock and environmental monitoring. It integrates, into a single portable unit, a solar power station, an edge-AI computer, a camera, environmental sensors, a LoRaWAN gateway, and a cellular router for backhaul. All parts are pre-wired in a modular enclosure, deployable by one operator in under 30 min. Data are fed to the agroNET farm-management platform and a purpose-built mobile web application; livestock detection runs on-device using a model from our earlier work. The system was evaluated at three sites, including a highland katun near Žabljak (~1450 m), under a two-phase energy-measurement protocol. During field logging it drew ~75 W on average against ~125 W solar input—a measured surplus that is used to recharge the battery—with a daily monitoring load of ~1560 Wh. The four-panel array’s nameplate potential in summer is an estimated ~3700 Wh/day, indicating substantial headroom relative to the measured load. At 80% depth of discharge the battery gives ~20 h autonomy, and the detection pipeline ran continuously, processing ~10,000 frames at under 3 s latency. The results demonstrate the feasibility of off-grid precision livestock farming, reaching TRL 6. Full article

Agrivoltaic systems, which enable simultaneous crop production and solar photovoltaic (PV) electricity generation on the same land, can support climate mitigation, food security, and rural development. Leguminous crops like beans are globally important, yet there is limited performance studies on diverse agrivoltaic trials. This limits appropriate policy guidance. To overcome these limitations, this study assessed organic green bush bean performance under thirteen PV configurations with varying transparency and spectral properties, comparing both agricultural outcomes against national yields and policy standards. The results in vegetative metrics indicated that blue-spectrum thin-film and intermediate-transparency c-Si modules supported growth near German productivity thresholds. Although no agrivoltaic system matched national average yields, combining crop and energy revenues revealed substantial benefits: the 44%—transparent c-Si configuration generated 340% more total revenue than traditional farming, and the blue 70%—transparent thin-film system achieved 94% of national yield but 164% of conventional farm revenue per acre. Electricity generation gains outweighed modest crop reductions, highlighting strong synergies between food and energy. The results of this study highlights the potential of agrivoltaic systems to enhance land-use efficiency, support renewable energy expansion, and improve rural economic resilience, while underscoring the need for multi-year trials and site-specific controls to validate long-term sustainability outcomes. Full article

The rapid development of wind and solar energy necessitates a solution to the problem of the optimal spatial placement of wind farms (WFs) and solar farms (SFs) within electric power systems. The non-stationary generation schedules of WFs and SFs place increased demands on the flexibility of conventional generation, determined by the intensity of net load fluctuations. This paper proposes a methodology for the spatial optimization of WF and SF location, in which the optimization criteria include net load indicators (rate of net load change and net load increment), the base power of the RES system, and the economic criterion of maximum electricity generation. Unlike existing approaches, in which the geographical smoothing effect on power fluctuations is treated as an incidental outcome, the proposed methodology employs it as an explicit optimization criterion for RES placement. The algorithm provides for the preliminary ranking of candidate sites based on the maximum electricity generation criterion, followed by the redistribution of generating capacities among sites with an acceptable capacity factor in accordance with the selected optimization criterion. The methodology was tested on a model comprising six potential wind farm sites and two solar farm sites with a total installed capacity of 600 MW and a maximum power system load of 3000 MW. The obtained results show that the optimal redistribution of installed capacities among sites allows a 31.5% reduction in net load variability intensity to be achieved with an 11.6% reduction in electricity generation relative to the maximum possible. The study is based on idealized daily generation and consumption profiles and is theoretical in nature, proposing a pre-screening tool for RES siting that complements rather than replaces subsequent network-constrained planning studies, including power-flow analysis and grid verification, and establishes a methodological foundation for further development using real multi-year retrospective data. Full article

Large portions of rural population in South Africa lack access to basic water and sanitation. This advocates for urgent interventions in support of water supply. This study assessed the performance of solar-powered groundwater pumping systems established at nine pilot sites in rural areas of Greater Giyani Municipality (Limpopo, South Africa). Performance assessment indicators, namely weather, groundwater abstraction, power supply, water supply, water quality, number of beneficiaries and farm productivity, were monitored (2023–2024). Increased groundwater abstraction reduced groundwater levels by 0.4–11 m, depending on the monitored borehole. This was replenished by above-average rainfall in 2023 (≈650 mm). Power supply and pump discharge rates were stable with generally low fluctuations at recommended pumping rates (0.5–2.0 L s⁻¹). Groundwater quality was generally fit to marginal for irrigation and drinking. High levels of NO₃⁻ and total organic carbon, especially in the proximity of villages, mandated the installation of mini water treatment plants for drinking water. The implementation of solar-powered groundwater pumping schemes was generally successful, with more than 5000 villagers benefiting directly from the interventions, whilst smallholder farms turned into commercial and financially viable enterprises. Long-term monitoring of bio-physical and socio-economic drivers is essential to ensure long-term sustainability of the solar-powered groundwater pumping systems. Full article

This study examines the economics of blue and green hydrogen as feedstock for large industrial facilities in Southeast Asia. To understand how industries can adopt low-emission and renewable hydrogen, the levelised costs of blue and green hydrogen are calculated. Four pathways are examined, including a large-scale carbon capture and sequestration facility located a distance away from an existing steam methane reforming hydrogen plant, a gigawatt-scale electrolysis facility adjacent to a large industrial site fed by an adjacent solar photovoltaic electricity source, as well as two pathways with either remote electrolyser and solar photovoltaic, necessitating hydrogen transport and storage, or a remote solar photovoltaic source with a dedicated power transmission line. The region’s transition to green hydrogen must overcome the challenges of high renewable electricity costs, the need for large land banks for solar photovoltaic farms and efficient long-distance hydrogen transport solutions or power transmission lines. Moreover, the region must improve its inconsistent track record in implementing billion-dollar public–private projects within budget and on time. Full article

The urgency to mitigate greenhouse gas emissions and address the accelerating impacts of climate change has placed renewable energy as a core part of global climate strategies. However, the expansion of renewable infrastructures with a focus on solar systems often generates competition with other land uses, raising concerns about land availability, environmental integrity, and social acceptance. Renewable energy solutions deployment must be aligned with sustainable land-use planning, particularly in diverse and multifunctional landscapes. This study presents a GIS-based Multi-Criteria Decision-Making (MCDM) methodology to identify the most suitable areas for implementing a set of six land-use-based adaptation and mitigation solutions (LAMSs) focused on solar energy. Using Python-based processing algorithms and high-resolution spatial datasets, the methodology integrates technical, environmental, and socioeconomic criteria to generate suitability maps for three different case studies across Europe: Almería (Spain), Valle d’Aosta (Italy), and the Azores (Portugal). Results reveal significant geographical disparities in suitability due to the different land constraints. Almería and the Azores demonstrate high potential for photovoltaic and agrovoltaic farms, while Valle d’Aosta’s mountainous terrain is more limited for these measures. Floating solar and solar land management measures show limited applicability across all sites. The analysis highlights the value of place-based approaches in energy planning and the utility of GIS-MCDM tools to support evidence-based decision-making, enabling context-sensitive deployment of renewable energy infrastructure. Full article

The large-scale deployment of photovoltaic (PV) systems increasingly faces land-use conflicts, particularly in regions with high environmental sensitivity resulting from intensive urban development. Consequently, decision-makers require transparent, spatially explicit tools to identify suitable areas for utility-scale PV installations (>100 kWp). This study addresses these challenges through the application of a Geographic Information System (GIS) to locate optimal sites for solar farms along the coastal zone of the Region of Murcia (southeastern Spain). First, the research characterizes the territorial context and systematically reviews the European, national, and regional regulatory frameworks to identify relevant legal and environmental constraints. These constraints are translated into thematic layers within the GIS environment and progressively applied to exclude unsuitable land through spatial editing and overlay analyses. The remaining feasible areas are subsequently evaluated according to their photovoltaic potential using publicly available solar resource data. The results show that nearly one quarter of the coastal territory is legally and environmentally suitable for PV deployment. Furthermore, due to the favourable geographical conditions of this Spanish region, the annual photovoltaic potential along the coastal zone reaches nearly 48,000 GWh, which would not only meet the Region of Murcia’s annual electricity demand (approximately 8000 GWh) but also supply neighbouring areas in southeastern Spain. Full article

Following successful applications in inland water bodies, floating photovoltaics (FPV) developers are now targeting offshore sites. This advancement requires numerical tools that can quantify the hydrodynamic performance of large-scale FPV farms. The existing wave-diffraction solver DIFFRAC was extended to simulate the response of a large number of interconnected floating objects on a supercomputer. The applicability is demonstrated by simulating a 2 MWp offshore solar farm, consisting of 3660 FPV modules moored inside a protective ring of 32 interconnected floating breakwaters (FBWs). The FPV motions and loads on FPV connectors in regular and irregular waves are compared to a reference case without FBW protection. Results show an average reduction in axial FPV connector loads in the setup with FBW ring, but local load enhancements occur due to dynamic amplifications of horizontal FPV module motions. Vertical loads and overturning moments onto FPV connectors are globally reduced by up to 50% in steep irregular seas but are locally enhanced due to standing waves that develop inside the ring. The insights of the hydrodynamic behaviour lead to recommendations for improving the farm configuration to further reduce fatigue and survival loads onto FPV modules and connectors. Full article

The chicken farm is a specific type of agricultural site with high electricity and heat consumption, which makes it ideal for the implementation of green energy. The specificity of the farm (need for continuous ventilation, lighting, and heating) allows achieving energy independence and reducing costs. Small farms can meet their own electricity needs using clean energy through the application of photovoltaics and converting waste biomass to usable energy. These two ways of power production could also reduce carbon footprints. In this study, the feasibility of using renewable energy for energy management in a poultry farm by consecutively involving solar and biomass energy was revealed. A biotechnological process for the production of biogas from chicken litter in a continuously stirred system of tank bioreactors was performed. It was supplied by electricity from a photovoltaic system. To obtain the maximum amount of solar energy, a photovoltaic system consisting of four panels, invertor and a battery with smart control was designed to collect, store, and bring energy to the reactor system collector and connected to the laboratory bioreactor, conveying the biogas production process. Several hydraulic retention times (HRT) were tested for optimizing biogas (biomethane) production, reaching a maximum of 575.49 NmL CH₄/dm³ at an HRT of 13.3 days for the first bioreactor and 278.7 NmL CH₄/g VS_add at an HRT of 120 days for the whole system. The energy balance made, reporting meteorological data, showed the economic feasibility for small farms to meet their own electricity needs. Involving renewable energy technologies could solve the problem of fossil fuel dependency and waste management for environmental protection and profit increase. It would permit a transition toward sustainable energy practices in agriculture and food production. Full article

While there is currently a significant opportunity for the construction of photovoltaic solar farms in the Southeastern United States, there is also a need for proper spatial planning that has not been adequately addressed by the existing literature. The objective of this study is to examine the adaptability of geographic information system-based multiple criteria decision analysis models developed for foreign contexts to the United States. This was accomplished through the application of a model developed originally for Thailand to the study area of Bibb County, Georgia, United States. Model results were analyzed to identify trends and provide concrete recommendations for future work. Using a six-rank classification scheme, 93% of Bibb County was found to have moderate suitability, while 5% and 2% had moderate-to-low and moderate-to-high suitability, respectively. Of the 11 model criteria, land usage and power line distance were found to have the largest impact on the area’s suitability. Statistical analysis identified positive trends indicating that these criteria explained 21% and 10% of the variance in the model’s output, respectively. Empirical verification proved the model structure to be viable for application in the Southeastern United States; however, additional examination of the model’s results found that there is room to improve the model for the local context. These improvements could potentially be realized through the reweighting of criteria and the re-establishment of evaluation benchmarks, allowing for the development of a truly robust model for the region. Full article

In the era of growing energy demand and the need to reduce greenhouse gas emissions, the development of renewable energy sources, including photovoltaic farms, is becoming a key element of a sustainable energy transition. In this context, the careful selection of cadastral plots on which farms can be built is crucial, as appropriate location influences the investment’s energy efficiency and minimizes environmental and planning risks. This article presents a proprietary methodology for identifying cadastral plots that are suitable for locating a photovoltaic farm. The presented methodology integrates the Fuzzy-AHP multi-criteria analysis method and the Fuzzy Membership fuzzy logic method, thereby reducing the subjectivity of expert assessments and improving the accuracy of estimating the values of factors considered in the research. A key element of the methodology is a detailed analysis of land and building register data, which results in the identification of specific plots with high investment potential. The multi-criteria analysis considered eight key factors related to climate, terrain, land cover, and cadastral data. Based on this, eight plots and 32 plot complexes were selected as the most suitable for the construction of PV farms. The most favorable locations were identified primarily in the eastern part of Częstochowa Poviat, as well as in the northern municipalities. The proposed methodology provides a ready-to-use, practical solution to the investment challenge of selecting specific cadastral plots for new solar investments. According to the reviewed literature, each of the 40 designated sites could support a photovoltaic farm of an estimated capacity of at least 1 MW. The obtained results provide significant input into the renewable energy investment planning process and emphasize that careful selection of plot locations is crucial for the investment’s success and the region’s sustainable energy transformation. Full article

This study investigates the efficacy of large language models (LLMs) in supporting responsible and optimized geographic site selection for large-scale solar energy farms. Using Microsoft Bing (predecessor to Copilot), Google Bard (predecessor to Gemini), and ChatGPT, we evaluated their capability to address complex technical and social considerations fundamental to solar farm development. Employing a series of guided queries, we explored the LLMs’ “understanding” of social impact, geographic suitability, and other critical factors. We tested varied prompts, incorporating context from existing research, to assess the models’ ability to use external knowledge sources. Our findings demonstrate that LLMs, when meticulously guided through increasingly detailed and contextualized inquiries, can yield valuable insights. We discovered that (1) structured questioning is key; (2) characterization outperforms suggestion; and (3) harnessing expert knowledge requires specific effort. However, limitations remain. We encountered dead ends due to prompt restrictions and limited access to research for some models. Additionally, none could independently suggest the “best” site. Overall, this study reveals the potential of LLMs for geographic solar farm site selection, and our results can inform future adaptation of geospatial AI queries for similarly complex geographic problems. Full article

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) frameworks to coastal urban microclimates, which involve intricate land-use dynamics and resilience constraints. To address this gap, this study proposes a multi-criteria GIS- based Analytical Hierarchy Process (AHP) framework, incorporating remote sensing and geospatial data, to assess Solar Farm Sites (SFSs) suitability, supplemented by sensitivity analysis in Thoothukudi coastal city, India. Ten parameters—covering photovoltaic, climatic, topographic, environmental, and accessibility factors—were used, with Global Horizontal Irradiance (18%), temperature (11%), and slope (11%) identified as key drivers. Results show that 9.99% (13.61 km²) of the area has excellent suitability, mainly in the southwest, while 28.15% (38.33 km²) exhibits very high potential along the southeast coast. Additional classifications include good (22.29%), moderate (32.41%), and low (7.16%) suitability zones. Sensitivity analysis confirmed photovoltaic variables as dominant, with GHI (0.25) and diffuse radiation (0.23) showing the highest impact. The largest excellent zone could support approximately 390 MW, with excellent and very high zones combined offering up to 2080 MW capacity. The findings also underscore opportunities for dual-use solar deployment, particularly on salt pans (17.1%), as well as elevated solar installations in flood-prone areas. Overall, the proposed framework provides robust, spatially explicit insights to support sustainable energy planning and climate-resilient infrastructure development in coastal urban settings. Full article

Global energy demand is steadily increasing, accompanied by a growing emphasis on clean and renewable energy sources. Serbia possesses significant solar energy potential, with solar radiation levels among the highest in Europe—about 40% above the European average. Within this context, rural depopulation clusters offer attractive opportunities for solar energy development due to the availability of underutilized land. This study aims to identify optimal locations for solar power installations in Serbia’s depopulated areas by applying multi-criteria decision-making methods under uncertainty. A hybrid framework, combining fuzzy Analytic Hierarchy Process (fuzzy AHP) and fuzzy MULTIMOORA, was employed to evaluate potential sites. Fuzzy AHP was used to determine the relative importance of criteria, while fuzzy MULTIMOORA ensured a robust ranking of alternatives by addressing the vagueness in data and expert judgments. The analysis identified several high-potential brownfield locations, with the most suitable land class covering 5.01% (16.94 km²) of the examined cluster area (311.3 km²). These areas are typically characterized by flat terrain, high solar irradiation, and minimal environmental constraints, providing favorable conditions for solar farms. Among the assessed sites, location no. 9 consistently ranked highest across all three fuzzy MULTIMOORA variants: FRPA (z = 0.0588), FRS (y = 0.2811), and FFMF (p = 1.6748). The findings confirm that the hybrid fuzzy AHP–MULTIMOORA approach offers valuable support for informed decision-making on solar energy deployment in depopulated rural regions. Moreover, the utilization of rural brownfield sites contributes to the expansion of renewable energy, rural revitalization, and sustainable land management in Serbia. Full article