Search Results (226)

Europe’s solar photovoltaic (PV) capacity is expanding rapidly, raising a key question: how much PV can each national electricity system actually absorb? Most existing assessments rely on annual or seasonal averages, which overlook the hour-by-hour match between PV generation and demand that ultimately limits feasible deployment. This study quantifies the demand-constrained PV potential of 38 European countries and how it varies across regions. Hourly PV generation is simulated in PVsyst and matched against national hourly demand from ENTSO-E. Feasible capacity is defined as the largest installation whose output never exceeds demand in any hour of the year. This system-level, time-resolved method yields operationally constrained estimates rather than purely physical potential. The 38 countries could feasibly deploy about 614 GWp of PV, generating around 678 TWh per year without exceeding hourly demand. Regional differences are pronounced: southern Europe benefits from superior solar resources, while northern and eastern regions face seasonal and infrastructural challenges. These findings underline the importance of grid modernization, energy storage, and cross-border integration. The estimates form a conservative baseline; they exclude drivers such as electric-vehicle (EV) deployment, demand-side flexibility, battery energy storage, latent demand growth, power export, and building-integrated photovoltaics (BIPV), whose inclusion would expand the feasible potential. This study offers a transparent comparative framework to guide policy, investment, and system planning for Europe’s carbon-neutral energy transition. Full article

One to four million annual premature deaths are associated with household air pollution. This indoor pollution is mainly generated by traditional biomass cookstoves. Thus, solar cooking can significantly reduce this toll. Its proliferation would also mitigate deforestation pressures. Additionally, for developing countries, it would alleviate the fuel collection workload, mainly borne by women responsible for fuel collection. Electric cooking provides a clean and controllable alternative to thermal cookers for indoor food preparation, sterilization and heating. This study presents a minimal, off-grid photovoltaic solar cooker that operates without batteries and power electronics. Such a cooker constitutes a low-cost and high-reliability solution for electrically decentralized locations. The system encompassing the cooker is conceived as an accessible entry point for household-level photovoltaic (PV) adoption. So, it offers the potential to catalyze the uptake of clean-energy technologies and to support sustainable development. The proposed design dissipates PV power into heat using commercial positive temperature coefficient (PTC) resistors operating near their Curie temperature. A simplified theoretical model is formulated to easily estimate the thermal power and heat-transfer conductances required for achieving cooking temperatures. An instrumented prototype allows for characterizing the transient temperature evolution during controlled heating and cooling experiments in the laboratory, facilitating development in an initial step avoiding the PV panel. The results demonstrate that the minimal PV configuration is technically feasible, robust, and compatible with low-resource settings. This encourages its adoption in communities experiencing energy poverty. Full article

This study evaluates the technical feasibility of an autonomous photovoltaic pumping system for agricultural use in isolated communities, using a representative region of the Mixteca Poblana, Mexico, as a case study. A reference sizing methodology reported in the literature was adopted for the sizing of isolated systems, and subsequently enhanced through a structured methodological extension, applied in the final stage of the design, focused on the technical validation and commercial selection of system components. The base framework incorporates site characterization and crop selection criteria. Subsequent stages define the hydraulic and electrical design requirements for the extension of the methodology, such as the calculation of water demand, the determination of pump power, and the estimation of energy requirements. These parameters enable the integrated correlation between hydraulic demand and electrical system constraints in the selection of the main system components, including the pump, photovoltaic array, battery storage system, water storage tank, and inverter. The technical robustness of the combined approach was validated through a simulation performed using specialized solar pumping software, confirming the operational feasibility and replication potential in rural communities with similar conditions. Full article

This paper presents an adaptive nonlinear control and state observation framework for energy management in standalone photovoltaic (PV) systems integrated with battery energy storage. A unified nonlinear dynamic model is developed to describe the interactions between the PV generator, the DC/DC buck converter, and the lithium-ion battery. Based on this model, a multi-mode control strategy is designed to ensure efficient and safe operation under varying environmental and loading conditions. The proposed scheme incorporates maximum power point tracking (MPPT) to maximize photovoltaic energy extraction, along with constant current (CC) and constant voltage (CV) charging modes to guarantee battery safety and longevity. To address uncertainties and unmeasured states, an adaptive nonlinear observer is developed for real-time estimation of the battery open-circuit voltage and state of charge. The observer design is supported by Lyapunov-based stability analysis, ensuring boundedness and convergence of the estimation error in the presence of modeling uncertainties and external disturbances. An energy management algorithm is further introduced to coordinate the transition between operating modes according to the estimated system states and battery constraints. The effectiveness and robustness of the proposed control and observation strategy are validated through detailed simulations in MATLAB/Simulink under varying solar irradiance conditions. The results demonstrate accurate maximum power tracking, reliable state estimation, and safe battery charging performance, highlighting the potential of the proposed approach for advanced autonomous PV–battery systems. Full article

This paper investigates the impact of renewable energy source (RES) integration on the Sicilian transmission network, considering the commissioning of new Mediterranean interconnections, namely the TUN-ITA and the Tyrrhenian Link. The expansion of transmission infrastructures and the increasing penetration of RES require an assessment of the Sicilian power system’s capability to accommodate high levels of power injection. This study was carried out in collaboration with the Italian transmission system operator Terna S.p.A. and the University of Palermo. It aims to evaluate the evolution of transmission line loading under future RES integration scenarios consistent with grid connection requests submitted to Terna and with national energy policy targets. The proposed methodology integrates micro-zonal assessments of wind and solar potential, estimation of capacity factors, development of RES capacity expansion scenarios, and steady-state power flow simulations. The simulations were performed using WinCreso^® software version 7.69 for three time horizons: 2028, 2029, and 2035. The results show the most congested transmission lines and the network areas most exposed to congestion. The analysis provides operational insights for prioritizing grid reinforcement measures and proposes a replicable methodological framework for other transmission system operators facing similar RES integration challenges. Full article

The widespread integration of rooftop solar photovoltaic systems into electricity distribution networks often leads to poor voltage regulation at user connection points, potentially breaching system voltage standards. Therefore, it is important for distribution network service providers to thoroughly assess such real and potential impacts to ensure compliant and safe operation of their power systems. The conventional approach of non-linear power flow-based voltage estimation using model-based methods is complex and time-intensive. Consequently, there is an increasing research interest towards model-free voltage estimation methods as a reliable alternative. This paper proposes and compares two distinct model-free voltage estimation approaches that can be utilised for effective hosting capacity estimation of residential solar photovoltaic systems in low voltage distribution networks. One approach utilises linear regression based on linearised power flow equations, while the other employs neural networks to capture non-linear power flow dynamics. The study developed 16 linear regression models and 648 neural network models utilising historical data collected from residential smart electricity metres in a real low voltage distribution network and compared their efficacy against conventional model-based, non-linear power flow simulations. Results indicate that the proposed model-free voltage estimation approaches can estimate voltages at user connection points in a similarly accurate but faster manner compared to the model-based approach. Observations show that the proposed linear regression-based voltage estimation approach is superior to the proposed neural network-based voltage estimation approach in terms of interpretability and practicality. Full article

Solar energy plays a fundamental role in achieving decarbonization in the construction sector, and therefore, a detailed assessment of solar potential at the urban scale is a key tool in supporting this process. Within this framework, the present study focuses on the high-resolution evaluation of photovoltaic (PV) potential in urban environments, specifically targeting the city of Milan, Italy, where two representative study areas are selected. In detail, 3D city models are developed using Rhino3D 7 software, and a solar radiation analysis was performed using Ladybug components. The solar radiation received by the surfaces that comprise the roofs and facades of buildings is estimated for each floor and orientation, taking into account local climate conditions and shadows cast by surrounding buildings. To define the economic viability of PV system deployment, two threshold criteria were introduced: one concerning the size (area) of the PV system and the other the minimum annual solar radiation level that each surface receives. Based on the obtained data, it is found that approximately 28% of roof surfaces and 5% of facades meet these cost-effective thresholds for PV integration. Further analysis indicates that the balcony self-shading can be considered negligible in the high-density urban context analyzed. The results are beneficial for urban energy management, considering energy savings and investment approaches, and the possibility to transform existing buildings into zero-carbon buildings powered by renewables. Full article

Wind-solar power generation is inherently uncertain. These uncertainties bring considerable difficulties to the assessment of hosting capacity. To tackle these difficulties, it is essential to create typical scenarios that can precisely capture the statistical traits and interrelationships of wind-solar power. In this research, we systematically integrate various scenario generation techniques, resulting in the creation of a holistic framework grounded in kernel density estimation (KDE) and Copula functions. Our proposed approach represents the stochastic nature of wind-solar power output by constructing their respective probability density functions (PDFs). It comprehensively depicts the potential spatiotemporal complementarity between wind-solar power by utilizing Copula functions and establishing a joint probability distribution model. Through Monte Carlo simulation, we generated a large number of wind-solar output scenarios. Subsequently, we employed the K-means clustering algorithm to reduce the number of scenarios. The findings reveal that the integrated framework, which combines KDE and Copula theory, achieves higher fitting accuracy for the marginal distributions and correlation structures of wind-solar power generation. As a result, the generated scenarios are more representative and reliable, offering strong support for photovoltaic (PV) hosting capacity analysis (HCA) and the formulation of typical plans. We validate the proposed method using historical wind-solar data from several representative regions in China, such as Inner Mongolia, northern Hebei, the Beijing–Tianjin–Hebei region, and Hubei Province. This validation demonstrates the method’s applicability under various geographical and climatic conditions. Full article

The transition toward sustainable energy in Arctic and subarctic regions requires innovative approaches that account for both the unique geographical conditions and the economic and policy challenges associated with isolated power systems. This study examines how net energy metering (NEM) and net billing schemes influence distributed solar photovoltaic (PV) adoption and financial performance among utilities in Alaska’s Railbelt. The Railbelt, which supplies power to three-quarters of the state’s population, remains heavily reliant on natural gas and exhibits limited renewable penetration compared to other arctic regions. Using a stochastic risk-based modeling framework with Monte Carlo simulations and the Bass diffusion model, the analysis estimates the 15-year financial impacts of different NEM adoption scenarios on utilities. Results show that while NEM drives PV adoption through higher compensation for exported generation, it also increases potential revenue losses for utilities compared to net billing. Policy innovations like those introduced in Alaska’s House Bill 164 (HB 164), which establishes a reimbursement fund to mitigate utility revenue losses, indicate that regulatory work is being designed to balance distributed generation incentives with economic sustainability. This work provides a baseline for understanding how a policy framework influences both utility and consumer economics in terms of NEM and solar PV adoption in Arctic and subarctic systems. Full article

In the face of accelerating climate change and urbanization, sustainable mobility infrastructure plays a critical role in reducing greenhouse gas emissions. This article assesses the Sunglider concept—an elevated, solar-powered transport system—through the New European Bauhaus (NEB) Compass, which emphasizes sustainability, inclusion, and esthetic value. Designed by architect Peter Kuczia and collaborators, Sunglider combines photovoltaic energy generation with modular, parametrically designed wooden pylons to form a lightweight, climate-positive mobility solution. The study evaluates the system’s technological feasibility, environmental performance, and urban integration potential, drawing on existing design documentation and simulation-based estimates. While Sunglider demonstrates strong alignment with NEB principles, including zero-emission operation and material circularity, its implementation is challenged by high initial investment, political and planning complexities, and integration into dense urban environments. Mitigation strategies—such as adaptive routing, visual screening, and universal station access—are proposed to address concerns around privacy, esthetics, and accessibility. The article positions Sunglider as a scalable and replicable model for mid-sized European cities, capable of advancing inclusive, carbon-neutral mobility while enhancing the urban experience. It concludes with policy and research recommendations, highlighting the importance of embedding infrastructure innovation within broader ecological and cultural transitions. Full article

The accurate short-term forecasting of global horizontal irradiance (GHI) is essential to optimizing the operation and integration of solar energy systems into the power grid. This study evaluates the performance of the Weather Research and Forecasting (WRF) model in predicting GHI over a 48 h forecast horizon at an Italian site: the ENEA Casaccia Research Center, near Rome (central Italy). The instantaneous GHI provided by WRF at model output frequency was post-processed to derive the mean GHI over the preceding hour, consistent with typical energy forecasting requirements. Furthermore, a decomposition model was applied to estimate direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) from the forecasted GHI. These derived components enable the estimation of solar energy yield for both concentrating solar power (CSP) and photovoltaic (PV) technologies (on tilted surfaces) by accounting for direct, diffuse, and reflected components of solar radiation. Model performance was evaluated against ground-based pyranometer and pyrheliometer measurements by using standard statistical indicators, including RMSE, MBE, and correlation coefficient (r). Results demonstrate that WRF-based forecasts, combined with suitable post-processing and decomposition techniques, can provide reliable 48 h predictions of GHI and DNI at the study site, highlighting the potential of the WRF framework for operational solar energy forecasting in the Mediterranean region. Full article

Urban energy systems in Andean cities face growing pressure to accommodate rising electricity demand while progressing toward decarbonization and grid modernization. Residential rooftop photovoltaic (PV) generation offers a promising pathway to enhance transformer utilization, reduce emissions, and improve distribution network performance. However, most GIS-based rooftop solar assessments remain disconnected from operational constraints of urban electrical networks, limiting their applicability for distribution planning. This study examines the technical and environmental feasibility of integrating residential PV distributed generation into the urban distribution network of an Andean city by coupling high-resolution geospatial solar potential analysis with monthly aggregated electricity consumption (MEC) and transformer loadability (LD) information. A GIS-driven framework identifies suitable rooftops based on solar irradiation, orientation, slope, shading, and three-dimensional urban geometry, while MEC data are used to perform energy-balance and planning-level transformer LD assessments. Results indicate that approximately 1.16 MW of rooftop PV capacity could be integrated, increasing average transformer LD from 21.5% to 45.8% and yielding an annual PV generation of about 1.9 GWh. This contribution corresponds to an estimated avoidance of 1143 metric tons of CO₂ per year. At the same time, localized reverse power flow causes some transformers to reach or exceed nominal capacity, highlighting the need to explicitly consider network constraints when translating rooftop solar potential into deployable capacity. By explicitly linking rooftop solar resource availability with aggregated electricity consumption and transformer LD, the proposed framework provides a scalable and practical planning tool for distributed PV deployment in complex mountainous urban environments. Full article

Accurately assessing rooftop photovoltaic (PV) potential requires precise identification of rooftop areas and availability. Current deep learning approaches using aerial imagery are faced with two challenges: inconsistent rooftop appearances caused by varying solar azimuths tend to mislead rooftop orientation extraction, and the existence of ancillary rooftop facilities often results in overestimation of solar potential. To tackle these challenges, a novel framework is proposed, with three components: automated extraction of rooftop areas and orientations, appearance-based estimation of rooftop availability coefficients, and PV potential calculation via a multi-orientation quantitative integration strategy. The segmentation network identifies geometric boundaries of rooftops and categorizes pitched roof segments into orientation-specific categories. High-level features of rooftop segments are then extracted from deep networks and clustered to compute availability coefficients at segment-level. Finally, the integration strategy leverages the symmetry assumption of sloped rooftops to mitigate classification errors and improve robustness in solar potential computation. Our framework is trained on the RID dataset with different category definition schemes, and estimation results are compared with solar radiation flux provided by NASA POWER. The overall relative error is less than 1%, which demonstrates the effectiveness of our framework. Full article

The global shift towards renewable energy is increasingly driven by the need to reduce carbon emissions and address urban energy demands sustainably. Solar power, as an accessible and efficient energy source, offers substantial potential for integration within urban environments. However, there remains a lack of a comprehensive evaluation framework for accurately predicting the energy generation of urban solar panel installations. Therefore, in this study, we develop a YOLO-based semantic segmentation framework to estimate the energy generation potential of existing solar panels in a city-scale fashion and use the Elephant andCastle area of London city as a case study. The results demonstrate that the proposed model can detect and segment solar panels in complex urban environments with an accuracy of 98.32%, and the total area of solar panels in the designated area is 127.75 m². Full article

Cu₂ZnSnSe₄ is a promising light-absorbing material for cost-effective and eco-friendly thin-film solar cells; however, its synthesis often leads to secondary phases that limit device efficiency. To overcome these challenges, we devised a straightforward and efficient method to obtain single-phase Cu₂ZnSnSe₄ nanocrystalline powders directly from the elements Cu, Zn, Sn, and Se via mechanochemical synthesis followed by vacuum annealing at 450 °C. Phase evolution monitored by X-ray diffraction (XRD) and Raman spectroscopy at two-hour milling intervals confirmed the formation of phase-pure kesterite Cu₂ZnSnSe₄ and enabled tracking of transient secondary phases. Raman spectra revealed the characteristic A₁ vibrational modes of the kesterite structure, while XRD peaks and Rietveld refinement (χ² ~ 1) validated single-phase formation with crystallite sizes of 10–15 nm and dislocation densities of 3.00–3.20 10¹⁵ lines/m². Optical analysis showed a direct bandgap of ~1.1 eV, and estimated linear and nonlinear optical constants validate its potential for photovoltaic applications. Scanning electron microscopy (SEM) analysis showed uniformly distributed particles 50–60 nm, and energy dispersive X-ray (EDS) analysis confirmed a near-stoichiometric Cu:Zn:Sn:Se ratio of 2:1:1:4. X-ray photoelectron spectroscopy (XPS) identified the expected oxidation states (Cu⁺, Zn²⁺, Sn⁴⁺, and Se²⁻). Electrical characterization revealed p-type conductivity with a mobility (μ) of 2.09 cm²/Vs, sheet resistance (ρ) of 4.87 Ω cm, and carrier concentrations of 1.23 × 10¹⁹ cm⁻³. Galvanostatic charge–discharge testing (GCD) demonstrated an energy density of 2.872 Wh/kg⁻¹ and a power density of 1083 W kg⁻¹, highlighting the material’s additional potential for energy storage applications. Full article