Search Results (6,886)

This extensive study examines the solar rooftop photovoltaic potential (RTP) over polluted cities in major geographic and economic zones of India. The study examines the climatology of solar radiation attenuation due to aerosol, clouds, architectural effects, etc. The study exploits earth observations from ground, satellite, and radiative transfer modeling (RTM) in conjunction with geographic information systems tools. The study exploits long-term observations of cloud properties from the Meteosat Second Generation (MSG) satellites operated by EUMETSAT and aerosol properties data gathered from ground-based measurements provided by AERONET. The innovation in the study is defined in two steps. Firstly, we estimated the RTP using the current state of the art in the field, which involved using suitability factors and energy output based on the PVGIS simulations and extrapolating these for effective rooftop areas of the cities. Secondly, we advanced beyond the current state of the art by incorporating roof morphological characteristics and various area share factors to assess the RTP in more realistic terms. These two steps were applied under two different scenarios. The study determined that the optimum tilt angle is equal to the cities’ latitude for installing solar PV systems. In addition, the research emphasizes the advantages for the environment while offering energy and economic losses. According to our findings, the RTP in the rural city examined in this study is 31% greater than the urban city of India under both scenarios. The research has found that the metropolitan city, which boasts a maximum rooftop area of approximately 167 km², could host a significant RTP of around 13,005 ± 1210.71 (6970 ± 751.38) MWh per year under scenario 1 (scenario 2). Overall, solar radiation losses due to aerosol effects dominate radiation losses due to cloud effects on the city scale. Amongst all polluted cities, estimated financial losses due to aerosols, clouds, and shadows are 11,241.70 million, 4439 million, and 1167.65 million rupees, respectively. Our findings emphasize the necessity of accounting for air pollution for accurate solar potential assessments in thoughtful city planning. The creative approach that utilizes publicly available data establishes a strong foundation for penetrating solar photovoltaic (PV) technology into society. This integration could significantly contribute to climate change mitigation and adaptation efforts, promoting environmentally sustainable urban development and prevention strategies. Full article

With global carbon emissions continuing to rise and urban energy demands growing steadily, understanding how urban block morphology impacts building photovoltaic (PV) efficiency and energy consumption has become crucial for sustainable urban development and climate change mitigation. Current research primarily focuses on individual building optimization, while block-scale coupling relationships between PV utilization and energy consumption remain underexplored. This study developed an integrated prediction and optimization tool using deep learning and physical simulation to assess how urban block design parameters (building morphology, orientation, and layout) affect PV efficiency and energy performance. Through a methodology combining block modeling, PV potential assessment, and energy consumption simulation, the research quantified relationships between design parameters, PV utilization, and energy consumption. Results demonstrate that appropriate building forms and layouts reduce shadow obstruction, enhance PV system capability, and simultaneously improve PV efficiency while reducing energy consumption. The tool provides improved prediction accuracy, enabling urban planners to scientifically design block layouts that maximize PV generation and minimize energy use. Extensive experimental validation demonstrates that the integrated model and analytical methods proposed in this study will help urban planners break through the limitations of individual building research, making PV-energy consumption optimization analysis at the block scale possible, and providing scientific basis for achieving low-carbon transformation and sustainable energy development in the building sector. Full article

This paper presents a novel maximum power point tracking (MPPT) method designed for a photovoltaic (PV) power system operating under partial shading conditions. Partial shading conditions induce multiple power peak characteristics into the power–voltage curve of the PV power system, such that conventional MPPT methods often lead local maximum power and result in suboptimal energy harvesting. To solve this problem, this paper proposes a chaotic artificial bee colony (CABC) algorithm hybridized with a chaotic searching behavior. The incorporation of the chaotic mapping enhances the exploration capability of bees (i.e., faster convergence time) and escapes local optima. To demonstrate its superior performance, the CABC algorithm is rigorously evaluated through simulations under two distinct partial shading scenarios, while making comparisons with the standard ABC algorithm and traditional MPPT methods. Therefore, the potential of this novel approach enhances MPPT accuracy, efficiency, and reliability in a partially shaded PV power system. Full article

The energy efficiency potential of a considerable number of Europe’s historical buildings is noteworthy. However, policymakers often express concerns about energy retrofits that may compromise the integrity of these structures and their surroundings. On the contrary, various strategies exist for enhancing energy efficiency in historic buildings without compromising their architectural constraints. The main aim of this study is to examine energy efficiency and retrofit strategies for historic commercial buildings in the UK. The case study that was selected is a historical building constructed in 1865 for the Water Works Company in the UK, whose function has changed through the years. The research methodology employed a combination of techniques that incorporated literature reviews, a case study, semi-structured interviews, and dynamic thermal simulations. For the purpose of obtaining reductions in emissions of greenhouse gases and consumption of energy, the energy performance of five different retrofit treatment methods that have the smallest damaging effect on historical significance was examined. This study demonstrates the effectiveness of integrating advanced building performance strategies, including wall enhancements, the optimisation of HVAC systems, and the implementation of minimally intrusive photovoltaic solutions. These interventions collectively contributed to achieving remarkable reductions in energy consumption, with electricity usage reduced by 100% and natural gas consumption decreased by 88.2%. Applying retrofit strategies reduced CO₂ emissions by approximately 95% from 20,493.51 kg to 1274.76 kg per year. The findings underscore that, despite the considerable potential for enhancing energy efficiency in historic structures, there exists an extensive absence of understanding among homeowners concerning accessible regulations, grants, and practical energy-saving measures. Full article

Arsenic contamination poses a severe health risk in regions with high water vulnerability and limited treatment infrastructure. This study evaluates a photovoltaic-powered water treatment system for arsenic removal in La Yarada Los Palos District, Tacna, Peru, where arsenic concentrations reached up to 0.0417 mg/L, significantly surpassing the World Health Organization (WHO) limit of 10 µg/L (0.01 mg/L) for drinking water. The system integrates a natural sedimentation pretreatment stage in a geomembrane-lined reservoir, followed by oxidation with sodium hypochlorite, coagulation, and adsorption. Arsenic removal efficiencies ranged from 99.72% to 99.85%, reducing residual concentrations below WHO guidelines. Pretreatment significantly improved performance, reducing turbidity by up to 66.67% and TSS by up to 70.37%, optimizing subsequent treatment stages. Operationally, pretreatment decreased cleaning frequency from six to four cleanings per month, while backwashing energy consumption dropped by 33% (from 45.72 kWh to 30.48 kWh). The photovoltaic system leveraged the region’s high solar radiation, achieving an average daily generation of 20.31 kWh and an energy surplus of 33.08%. The system’s performance was evaluated within the context of existing arsenic removal technologies, demonstrating that the integration of natural sedimentation and renewable energy constitutes a viable operational alternative. Given the regulatory framework in Peru, where arsenic limits align with WHO standards, conventional water treatment systems are normatively and technically unfeasible under national legislation. Furthermore, La Yarada Los Palos District faces challenges due to its limited infrastructure for conventional electrification via power grid, as identified in national reports on rural electrification and gaps in access to basic services. Beyond its performance in the study area, the system’s modular design allows adaptation to diverse water sources with varying arsenic concentrations, turbidity levels, and other physicochemical characteristics. In remote regions with limited access to the power grid, such as the study site, photovoltaic energy provides a self-sustaining and replicable alternative, particularly in arid and semi-arid areas with high solar radiation. These conditions are not exclusive to Latin America but are also prevalent in remote regions of Africa, the Middle East, Asia, and Oceania, where groundwater arsenic contamination is a significant issue and renewable energy availability can enhance water treatment sustainability. These findings underscore the potential of using sustainable energy solutions to address water contamination challenges in remote areas. The modular and scalable design of this system enables its replication in regions with adverse hydrogeological conditions, integrating renewable energy and pretreatment strategies to enhance water treatment performance. The framework presented in this study offers a replicable and efficient approach for implementing eco-friendly water treatment systems in regions with similar environmental and resource constraints. Full article

The specific research presents a detailed, data-driven energy analysis on the early-life operation of two rooftop photovoltaic (PV) systems. The two PV systems examined are operated under the Net Metering scheme and are found in close proximity, within the geographical boundaries of a small-scale remote island on the Southeastern part of the Aegean Sea, Greece. The systems feature similar PV technology and capacity, i.e., 3.5 kW_p vs. 5 kW_p, but differ in terms of installation characteristics; thus, they offer an interesting case for pairwise comparison. Supported by the exploitation of a wealthy set of data, a detailed energy analysis is conducted, with our results providing useful insights on the seasonal performance variation of the two systems and its determinants, reflecting on the different siting and installation characteristics of the former. Full article

Transformerless inverters (TIs) are becoming increasingly popular in solar photovoltaic (PV) applications due to their enhanced efficiency and cost-effectiveness. Unlike transformer-based inverters, TIs, which lack transformers and additional components, offer significant advantages in terms of reduced weight, compactness, and lower costs. Research studies have demonstrated that multilevel TIs can achieve lower total harmonic distortion (THD), reduced switching stresses, and higher AC output voltage levels suitable for high voltage applications. However, achieving these outcomes simultaneously with maximum power ratings and the lowest switching frequencies poses a challenge for TI topologies. In light of these challenges, this research proposes the implementation of a 13-level single-source switched-capacitor boost multilevel inverter (SSCBMLI) designed for solar PV systems. The SSCBMLI consists of a single DC power source, switched-capacitor (SC) units, and a full H-bridge. Compared to other existing 13-level multilevel inverter (MLI) configurations, the proposed SSCBMLI utilizes the fewest components to minimize development costs. Moreover, the SSCBMLI offers voltage boosting and can drive high inductive loads, self-voltage-balanced capacitors, an adaptable topology structure, and reliable system performance. Simulations and experimental tests are conducted using PLECS 4.5 and SIMULINK to assess the performance of the proposed SSCBMLI under varying modulation indices, source powers, and loads. A comparative analysis is then conducted to evaluate the SSCBMLI against existing inverter topologies. Full article

A new and improved sliding mode control (NISMC) with a grey linear regression model (GLRM) facilitates the development of high-quality pure sine wave inverters in photovoltaic (PV) energy conversion systems. SMCs are resistant to variations in internal parameters and external load disturbances, resulting in their popularity in PV power generation. However, SMCs experience a slow convergence time for system states, and they may cause chattering. These limitations can result in subpar transient and steady-state performance of the PV system. Furthermore, partial shading frequently yields a multi-peaked power-voltage curve for solar panels that diminishes power generation. A traditional maximum power point tracking (MPPT) algorithm in such a case misclassifies and fail to locate the global extremes. This paper suggests a GLRM-based NISMC for performing MPPT and generating a high-quality sine wave to overcome the above issues. The NISMC ensures a faster finite system state convergence along with reduced chattering and steady-state errors. The GLRM represents an enhancement of the standard grey model, enabling greater accuracy in predicting global state points. Simulations and experiments validate that the proposed strategy gives better tracking performance of the inverter output voltage during both steady state and transient tests. Under abrupt load changing, the proposed inverter voltage sag is constrained to 10% to 90% of the nominal value and the voltage swell is limited within 10% of the nominal value, complying with the IEEE (Institute of Electrical and Electronics Engineers) 1159-2019 standard. Under rectified loading, the proposed inverter satisfies the IEEE 519-2014 standard to limit the voltage total harmonic distortion (THD) to below 8%. Full article

Water electrolysis is a potential contributor to global decarbonization, enhancing the flexibility and resilience of the electricity system and enabling integration with different sectors, such as industry and transportation, by acting as an energy vector and storage, as well as chemical feedstock. This study investigates the potential of hydrogen production by electrolysis in future national electric grid scenarios for Italy as a case study. It examines the impact of increasing photovoltaic and wind capacities up to five times the 2019 levels, considering an electricity storage capacity of up to 200 GWh. The feasibility of fully meeting current national hydrogen consumption through electrolysis in these scenarios is assessed by considering different overall electrolysis capacities. Specific CO₂ emissions associated with hydrogen production are evaluated as an indicator of environmental feasibility and compared with the conventional steam methane reforming. In addition, the levelized cost of hydrogen production is evaluated as an indicator of economic feasibility. Some limitations of electrolysis emerge when it is considered the sole way to decarbonize hydrogen production. Very high renewable shares are required to make electrolysis alone a feasible solution. Aiming to maximize the use of renewable curtailment for electrolysis conflicts with maximizing the electrolyzers’ utilization factor, thus, negatively affecting hydrogen production costs. Furthermore, since priority is given to the use of renewable and stored electricity to meet electricity demand, the remaining electricity is insufficient to produce the entire hydrogen demand in most of the considered scenarios, particularly when substantial storage supports the grid, as this reduces the curtailment available for electrolysis. Full article

The evolution of aircraft power systems has been driven by increasing electrical demands and advancements in aviation technology. Background: This study provides a comprehensive review and experimental validation of on-board electrical network development, analyzing power management strategies in both conventional and modern aircraft, including the Mi-24 helicopter, F-22 multirole aircraft, and Boeing 787 passenger airplane. Methods: The research categorizes aircraft electrical systems into three historical phases: pre-1960s with 28.5 V DC networks, up to 2000 with three-phase AC networks (3 × 115 V/200 V, 400 Hz), and post-2000 with 270 V DC networks derived from AC generators via transformer–rectifier units. Beyond theoretical analysis, this work introduces experimental findings on hybrid-electric aircraft power solutions, particularly evaluating the performance of the Modular Power System for Aircraft (MPSZE). The More Electric Aircraft (MEA) concept is analyzed as a key innovation, with a focus on energy efficiency, frequency stability, and ground power applications. The study investigates the integration of alternative energy sources, including photovoltaic-assisted power supplies and fuel-cell-based auxiliary systems, assessing their feasibility for aircraft system checks, engine startups, field navigation, communications, and radar operations. Results: Experimental results demonstrate that hybrid energy storage systems, incorporating lithium-ion batteries, fuel cells, and photovoltaic modules, can enhance MEA efficiency and operational resilience under real-world conditions. Conclusions: The findings underscore the importance of MEA technology in the future of sustainable aviation power solutions, highlighting both global and Polish research contributions, particularly from the Air Force Institute of Technology (ITWL). Full article

The HERMES project investigates the utilization of surplus wind and solar energy to produce renewable fuels such as hydrogen, methane, and methanol for seasonal storage, thereby supporting carbon neutrality and the energy transition. This initiative aims to create a closed-loop, zero-emission energy system with efficiencies of up to 65%, employing a low-pressure (≤30 bar) synthesis process—specifically, sorption-enhanced methanol synthesis—integrated into the power system. Excess renewable electricity is harnessed for chemical synthesis, beginning with electrolysis to generate hydrogen, which is then converted into methanol using CO₂ sourced from a biogas plant. This methanol, biomethane, or a hybrid fuel blend powers a supercritical gas turbine, providing a flexible and reliable energy supply. Optimization analysis indicates that a combined wind and photovoltaic system can meet 62% of electricity demand, while the proposed storage system can handle over 90%. Remarkably, liquid methanol storage requires a compact 313 m³ tank, significantly smaller than storage requirements for hydrogen or methane in gas form. The project entails a total investment of 105 M EUR and annual operation and maintenance costs of 3.1 M EUR, with the levelized cost of electricity expected to decrease by 43% in the short term and 69% in the long term as future investment costs decline. Full article

In this study, a neural network-based power prediction for a photovoltaic system was conducted using a multi-parameter approach, considering radiation, temperature, wind speed, humidity, and cloud cover. Photovoltaic systems are highly popular renewable energy sources due to their robust, modular, and environmentally friendly characteristics. Although photovoltaic systems offer many advantages, their dependency on irradiation for energy generation and their sensitivity to meteorological parameters pose a significant disadvantage, leading to intermittent energy production. Since these parameters affect the quality of power generated at the plant, they introduce uncertainty in power systems. Therefore, it is crucial to consider these factors in energy planning and management. In this study, to mitigate uncertainty in power systems and contribute to energy planning by predicting power production, power data obtained from a power plant, along with meteorological data, were used in Single Layer Perceptron Neural Network. The predicted power values obtained from the proposed model were compared with the actual values, and the results of this comparison were presented. Furthermore, to demonstrate the model’s performance, the R and MSE values were provided as 0.98 and 0.03, respectively, indicating a strong correlation between predicted and actual values and a low prediction error. Full article

Punjab, the most populous province in Pakistan, is currently facing substantial electricity shortages that are adversely affecting both residential and industrial sectors. To address this issue, the Cholistan Desert presents a promising solution due to its high solar irradiance, making it an ideal location for solar energy production. This study aims to identify the most suitable area for solar photovoltaic (PV) power plants in the Cholistan Desert using Geographic Information System (GIS) and machine learning techniques. The analysis included field survey data encompassing 14 conditioning factors such as geophysical, socio-economic, and resource conditions. Three machine learning models were utilized: Random Forest, XGBoost, and Multilayer Perceptron (MLP). The Random Forest model demonstrated superior performance with an AUC of 0.92, and feature importance was measured through SHAP. The resulting suitability map indicates that Bahawalnagar in the eastern region and Bahawalpur in the central region have 10.50% and 11.06% of their areas classified as having a “high” and “very high” probability for solar PV installation, respectively. For stakeholders in the wind industry, these regions also present potential for wind farm feasibility due to favorable wind conditions and flat terrain. The methodology can be adapted to prioritize wind energy sites by incorporating factors such as land availability, wind direction, and other related factors. Co-locating solar and wind farms in these regions could optimize land use, enhance grid stability, and support Pakistan’s renewable energy targets. Future research integrating real-time solar and wind data could further refine site selection and support multi-source renewable energy planning, providing actionable insights for policymakers and investors. Full article

Maximizing the energy output of photovoltaic (PV) systems is becoming increasingly important. Consequently, numerous approaches have been developed over the past few years that utilize remote sensing data to predict or map solar potential. However, they primarily address hypothetical scenarios, and few focus on improving existing installations. This paper presents a novel method for optimizing the tilt angles of existing PV arrays by integrating Very High Resolution (VHR) satellite imagery and airborne Light Detection and Ranging (LiDAR) data. At first, semantic segmentation of VHR imagery using a deep learning model is performed in order to detect PV modules. The segmentation is refined using a Fine Optimization Module (FOM). LiDAR data are used to construct a 2.5D grid to estimate the modules’ tilt (inclination) and aspect (orientation) angles. The modules are grouped into arrays, and tilt angles are optimized using a Simulated Annealing (SA) algorithm, which maximizes simulated solar irradiance while accounting for shadowing, direct, and anisotropic diffuse irradiances. The method was validated using PV systems in Maribor, Slovenia, achieving a 0.952 F1-score for module detection (using FT-UnetFormer with SwinTransformer backbone) and an estimated electricity production error of below 6.7%. Optimization results showed potential energy gains of up to 4.9%. Full article

The advantages of green roofs and solar panels are numerous, but in dry periods, green roofs can place urban water resources under pressure, and the efficiency of solar panels can be affected negatively by high temperatures. In this context, our analysis investigated the advantages of bio-solar green roofs and evaluated the impact of green roofs on solar panel electricity production and solar panels on green roof water consumption. The assessment was conducted through simulation in a selected case study located in Cosenza, a city with a Mediterranean climate, with solar panels covering 10% to 60% of the green roof. Analyses were performed on the power outputs of four kinds of photovoltaic panels: polycrystalline, monocrystalline, bifacial, and Passivated Emitter and Rear Contact (PERC). The energy production and shade frequencies were simulated using PVGIS 5.3 and PVSOL 2024 R3. The impact of photovoltaic (PV) shade on the water consumption of green roofs was evaluated by image processing of a developed code in MATLAB R2024b. Moreover, water–energy interconnections in bio-solar green roof systems were assessed using the developed dynamic model in Vensim PLE 10.2.1. The results revealed that the water consumption by the green roof was reduced by 30.8% with a bio-solar coverage area of 60%. However, the electricity production by the PV panel was enhanced by about 4% with bio-solar green roofs and was at its maximum at a coverage rate of 50%. This investigation demonstrates the benefits of bio-solar green roofs, which can generate more electricity and require less irrigation. Full article