Search Results (9,403)

Photovoltaic (PV)–building systems are commonly assessed using aggregated energy indicators that obscure the structural diversity of energy flows. This study introduces an entropy-based approach for quantifying the operational structure of energy flows in a real PV–building system from an information-theoretic perspective. High-resolution 15-min measurement data covering a full month were used to derive cumulative energy contributions associated with local self-consumption, surplus export to the grid, and electricity import from the grid. These flows were reformulated as a probability distribution and analyzed using Shannon entropy. The results reveal a high normalized entropy of monthly energy flows, indicating substantial dispersion among operating states despite the dominance of local self-consumption. Entropy decomposition shows that all flow components contribute meaningfully to system uncertainty, including surplus export, which exhibits a disproportionate informational impact relative to its energy share. The findings demonstrate that entropy captures structural properties of PV–building operation not accessible through conventional energy balance metrics. The proposed framework provides a compact, scale-independent descriptor of operational complexity and offers a new quantitative perspective for assessing predictability and structural heterogeneity in PV–building energy systems. Full article

Long-term energy system models often represent renewable energy technologies using aggregated potentials and average capacity factors, which may insufficiently reflect the spatial and technological heterogeneity of weather-dependent resources. This study develops and implements resource- and performance-based renewable energy supply curves for solar photovoltaics, onshore wind and offshore wind in the TIMES-PL energy system model for Poland. These supply curves are coupled with time-dependent techno-economic assumptions in TIMES-PL, allowing the modelled attractiveness of individual renewable resource classes to change across model years. The proposed approach combines spatial resource assessment, GIS-based data processing and differentiated hourly capacity factor profiles. The supply curves were constructed using data from the JRC ENSPRESO database, the PVGIS interface and the Copernicus Climate Data Store, with QGIS applied to classify renewable resource potential according to regional conditions, wind farm location and photovoltaic panel orientation. Two model scenarios were compared: a base scenario without supply curves and a scenario with implemented supply curves. The results show that incorporating spatial and technological constraints changes the modelled optimal capacity mix, although the overall system-level differences remain moderate. Accordingly, the results should be interpreted primarily in terms of installed capacity expansion rather than as a full comparison of system costs, electricity generation, unit dispatch or balancing effects. The total installed capacity in the supply-curve scenario is 1.91–3.44 GW higher than in the base scenario, corresponding to less than 3% of total system capacity. This increase results from the model being required to use renewable resource classes with lower capacity factors once the most favourable potentials are fully utilised. This study demonstrates that renewable energy supply curves can improve the representation of spatially differentiated renewable deployment options in long-term national energy system modelling. Full article

High electricity prices remain a substantial barrier to electric vehicle (EV) diffusion. To address this challenge, we propose a bundled sales model that integrates EVs with distributed, operationally off-grid photovoltaic (PV) systems for self-consumption. Using a sequential game-theoretic framework and scenario analysis calibrated to U.S. and German data, we show that, within the calibrated scenarios and declared system boundaries, bundling accelerates EV adoption and reduces modeled oil dependency, measured as the physical volume of fossil fuel displaced by the bundled fleet. In Germany, bundling increases oil-dependency reduction by 7.8 percentage points, to 34.5%, relative to the traditional unbundled model. The bundled model also delivers stronger decarbonization, yielding incremental lifecycle emission reductions of 11% in the U.S. and 29% in Germany under the declared system boundary. Three insights follow. First, bundling is especially advantageous in markets with high grid tariffs, strong solar irradiance, or falling PV costs. Second, decoupling EV charging from carbon-intensive grids promotes household energy self-sufficiency and helps households become more resilient energy prosumers. Third, the threshold analysis indicates that the model is already viable in high-tariff markets such as Germany, while declining battery costs are likely to trigger a tipping point in lower-tariff markets such as the U.S., supporting a gradual diffusion pattern from suburbs to cities. These findings identify a viable pathway for low-carbon transport transitions through synergistic EV–solar integration. Full article

This paper introduces a hybrid building-integrated photovoltaic façade that brings together a 240 Wp mono PERC photovoltaic (PV) module and an electrically switchable Polymer Dispersed Liquid Crystal glazing unit within a single layered component. The main novelty of the proposed system is that the PV module and the smart glass layer are not treated as independent façade elements; instead, they are integrated to create a direct optical and functional interaction within the same building envelope component. The design enables controlled interaction between solar radiation, the PV cells, and the interior environment. By adjusting the optical state of the glazing, the façade can influence the amount of light entering the building while also affecting the irradiance received by the PV cells. In this way, the proposed façade provides two adaptive operating modes: a transparent mode that supports daylight transmission and indoor visual comfort, and a non-transparent mode that enhances effective irradiance on the PV cells through diffuse reflection. Experimental investigations carried out under real outdoor conditions, together with finite element thermal modeling, are used to evaluate electrical output and temperature behavior. Unlike conventional BIPV or smart glass applications, the proposed structure simultaneously addresses electricity generation, daylight regulation, and thermal management in a single multifunctional façade system. The results indicate that the integrated configuration yields nearly 4% higher energy production compared to a reference PV module, and that electrical output increases by about 5% when the glazing is in its non-transparent state. These findings highlight the potential of the proposed system to enhance both on-site electricity generation and indoor environmental performance in sustainable building applications. Full article

This research evaluates the feasibility of implementing photovoltaic solar systems in the Ecuadorian Amazon to harness solar energy and increase energy security in the region. It is based on the need to reduce direct dependence on fossil fuels and existing hydroelectric systems. The overall framework is to transform the energy matrix to utilize incident solar energy, integrating it with current hydroelectric and thermal generation. The fundamental goal is to evaluate the energy resource using specialized software such as Homer Pro and develop designs for the proper operation of photovoltaic solar technology, which will contribute its surplus energy to the National Interconnected System (SNI) and, therefore, reduce the country’s high dependence on the hydrological cycle. The results obtained demonstrate that solar power plants can be of great benefit to the country, especially when combined with wind and existing hydroelectric power. This will contribute to the diversification of energy sources and, consequently, to energy security through the increase in renewable energy. In the worst-case scenario, the cost of energy can be 7 cents per kWh, and in the best-case scenario, in a combined dispatch, 3 cents per kWh. Full article

The rapid expansion of decentralized photovoltaic (PV) systems is transforming electricity consumers into active market participants across Europe. While prosumer development has been extensively studied in Western Europe, the dynamics, constraints, and transition pathways of emerging Eastern European markets remain insufficiently understood. This study provides a comprehensive assessment of prosumer ecosystem development in Romania as one of the fastest-growing prosumer markets in Europe and a representative case for broader Eastern European energy transitions. A novel integrated analytical framework is proposed, combining modified logistic diffusion modelling, a Policy Support Index (PSI), a Grid Saturation Index (GSI), and qualitative stakeholder-based barrier assessment. Unlike conventional approaches, the framework explicitly incorporates policy responsiveness and infrastructure constraints into prosumer diffusion analysis. Results reveal an exceptional acceleration of prosumer deployment, with cumulative installations increasing from 303 prosumers and 1.52 MW in 2019 to 339,107 prosumers and 5311.96 MW by April 2026. Diffusion modelling identifies three transition phases—market formation, rapid expansion, and saturation-driven moderation—and estimates an effective carrying capacity of approximately 398,000 prosumers under current institutional and grid conditions. The PSI increased from 0.22 to 0.80 between 2019 and 2026, highlighting the GSI deteriorated from 0.96 to −0.59, indicating severe distribution-grid saturation. Institutional and technical barriers emerged as the most critical constraints, surpassing market and behavioral factors. The findings demonstrate that prosumer growth in Eastern Europe establishes a constrained socio-technical transition rather than a purely market-driven process. The proposed framework offers a transferable methodology for evaluating prosumer ecosystems and provides evidence that future expansion will depend increasingly on grid modernization, energy storage deployment, and adaptive regulatory governance. Full article

Accurate forecasting of power production and consumption is essential for the efficient operation of smart energy grids, enabling stable energy exchange and grid reliability. However, the growing integration of photovoltaics (PVs) and electric vehicles introduces significant uncertainty. This paper evaluates multiple Machine Learning (ML) models for single- and multi-step forecasts of PV generation and household consumption, incorporating uncertainty bounds to inform operator decisions. We use data from two Swedish sites and the CityLearn benchmark dataset to compare direct, recursive, and hybrid multi-step strategies. LightGBM with gradient-boosted quantile regression achieves the best single-step performance, with Mean Absolute Error (MAE) as low as 10.19 W in Halmstad and 16.12 W in Uppsala. For multi-step forecasts, the direct method outperforms others, reaching a 48 h consumption MAE of 71.08 W in Uppsala and 52.05 W in Halmstad, and achieving prediction interval coverage probabilities above 0.95. Moreover, personalized models trained on individual households outperform generalized ones, even with smaller datasets, highlighting the value of tailored approaches for improving forecast accuracy under uncertainty. Full article

The decentralized deployment of photovoltaic (PV) systems in urbanized polluted mountainous basins faces unique challenges due to complex topography, persistent cloud cover and winter smog conditions. This study quantifies the atmospheric impact of localized winter haze/smog and Saharan dust intrusions on PV performance in the intermontane basin of Ioannina, NW Greece. By integrating a Digital Twin (DT) methodology with real energy production data, two PV plants were evaluated, a ground-based and a rooftop installation, to isolate the energy deficits caused by aerosol attenuation. The DT model demonstrated high accuracy (R² = 0.847) against actual power generation data for Koutselio and R² = 0.865 for Mpafra PV plants, while MBE was near zero for both sites (−0.008 kWh and −0.139 kWh, respectively). Error analysis revealed that the highest modeling discrepancies occurred during scattered clouds and intense winter haze conditions, primarily due to low spatial resolution of CAMS that fails to adequately capture localized biomass burning (ΒΒ) events. Despite the reduction in direct sunlight during extreme winter BB events, results indicate that the overall energy loss is mild. This operational stability is primarily due to the ability of c-Si modules to effectively utilize near-infrared radiation, which penetrates the low-level haze layer, alongside the thermal efficiency gains provided by low early-morning temperatures. Crucially, the installation geometry may influence system vulnerability. Direct comparisons revealed a minor power deviation of –4.8% for the ground-based Koutselio plant, while for the Mpafra site, there was a +3.2% production surplus likely linked to the high sky-view factor the rooftop installation has, which manages to capture isotropic diffuse irradiance. However, the low CAMS resolution may misclassify the haze events within the basin, further contributing to these discrepancies. On the contrary, Saharan dust intrusions caused broadband light attenuation, dropping the power production significantly on both installations. Ultimately, this research provides critical insights into the resilience of solar systems under strong air pollution events within polluted valleys in Southern Balkans, highlighting the connection between panel design and atmospheric attenuation. Full article

Existing DG allocation studies commonly rely on static or balanced feeder assumptions, leaving two practical issues insufficiently addressed: the hourly mismatch between renewable outputs and feeder demands and diesel-backup phase-imbalance safety in unbalanced networks. This paper presents a safety-aware allocation framework for hybrid photovoltaic (PV) and wind turbine (WT) systems in unbalanced three-phase feeders. The methodology explicitly accounts for 24 h generation–load coordination and diesel backup operating limits through two post-load-flow indicators: load-following error (LFE), which measures the hourly mismatch between aggregate distributed generation (DG) outputs and a load-proportional target, and power unbalance ratio (PUR), which limits diesel-unit phase-power imbalance to 10% during dispatch. The constrained siting and sizing problem is solved using a genetic algorithm (GA) on the IEEE 37-bus feeder under realistic diurnal load, solar, and wind profiles. While an unconstrained allocation achieves 64.57% active-power loss reduction, it exceeds the adopted diesel PUR screening threshold. Enforcing the PUR constraint yields a feasible operating state with 60.87% loss reduction, retaining 94.27% of the unconstrained benefit. Robustness checks across 30 independent runs and GA/PSO/ACO benchmarking confirm that the adopted GA provides the lowest dispersion and highly repeatable feasible outcomes. The results show that the framework improves energy efficiency, voltage regulation, and daily coordination while satisfying the adopted diesel phase-power screening criterion under severe feeder asymmetry. Full article

Third-generation photovoltaic (PV) technologies, such as dye-sensitized solar cells (DSSCs), organic solar cells (OSCs), quantum-dot solar cells (QDSSCs), and perovskite solar cells (PSCs), are characterized by properties that enable applications beyond conventional silicon-based devices. However, despite remarkable progress in third-generation solar cells, significant challenges related to efficiency, stability, scalability, and commercialization remain significant. The purpose of this review work was to summarize recent developments in third-generation photovoltaic technologies, including component materials design, configurations, performance data, limitations, and future research directions. The reported studies demonstrated crucial improvements in power conversion efficiency, which exceeded 15% for DSSC, 20% for OSC, 12% for QDSSC, and 26% for PSC. The key challenges to commercialization include further improvements in efficiency, better stability, and meeting the environmental requirements. Although important technological and environmental challenges remain, third-generation solar cells are expected to contribute to future sustainable energy systems due to their high efficiency potential, low-cost fabrication, and possible incorporation of environmentally friendly materials in the structure of the cells. The photovoltaic performance under indoor conditions and the aspect of a sustainable approach were identified as recent research trends. Full article

The global expansion of renewable energy has increased the strategic importance of photovoltaic (PV) power plants and the demand for comprehensive, high-quality solar datasets to support energy planning, optimization, and data-driven applications. However, existing datasets are often constrained by limited attribute integration, incomplete information, and insufficient global coverage. This study presents a standardized, reliable, and multidimensional global dataset for photovoltaic power plant analysis, together with a largely reproducible and documented methodology that automates the collection, generation, and integration of heterogeneous solar-related data from multiple sources. Using this methodology, 27 geographic, topographic, logistical, climatic, and power-related attributes were integrated into a unified dataset comprising 58,978 photovoltaic plant records worldwide. Descriptive statistical analyses were performed to characterize the dataset and assess its informational richness and consistency. The results demonstrate that both the proposed methodology and the resulting dataset provide a robust foundation for photovoltaic energy research and decision-making across diverse application domains. By making this resource publicly available, this work facilitates reproducible research and supports the development of advanced analytical, predictive, and optimization models for academic, industrial, and policy-oriented applications. Full article

To address the bottleneck that traditional building blinds struggle with, namely synergistically achieve shading control and energy recovery, a vertical mid-pane photovoltaic (PV) louver based on a self-powered feedback mechanism was designed. This system utilizes the voltage difference generated by differential light exposure on photovoltaic thin-film cells to drive a motor, realizing zero-energy automatic tracking of the solar azimuth and dynamic adjustment of component angles. By establishing a mathematical model for sunlight-tracking power generation and combining it with COMSOL multiphysics simulation, the coupling effects of the PV louver angle, operating conditions, and solar terms on photoelectric performance were thoroughly analyzed. The research results indicate that when the PV louver angle increases from 60° to 150°, the power generation significantly improves by 80%. Compared with the non-tracking mode, the all-day power generation efficiency gain of the vertical tracking center-mounted PV louver can reach up to 19.68%. Driven by the seasonal evolution of the solar elevation angle, the direct radiation irradiance during the tracking period across four typical solar terms exhibits a distribution pattern characterized as “higher in winter, lower in summer, and intermediate in spring and autumn.” These findings provide a technical pathway integrating dynamic shading, passive photothermal regulation, and clean power generation for south-facing facades in hot summer and cold winter zones, offering significant reference value for enhancing the energy autonomy and low-carbon level of building envelopes. Full article

Scrap-based electric arc furnace (EAF) steelmaking powered by on-site variable renewable energy is a key decarbonisation route, but the heteroscedastic, non-Gaussian nature of joint wind–photovoltaic forecast errors makes the EAF—a large, metallurgically constrained load—hard to coordinate with on-site generation under feeder limits. We develop a unified stochastic dispatch and adaptive setpoint-control framework. A chance-constrained dispatch over a zone-wise Beta uncertainty model is propagated through a degree-two polynomial chaos expansion (PCE) and reformulated as a second-order cone programme via the Cantelli inequality, with EAF-specific metallurgical constraints (electrode slew, short-circuit-ratio-tied flicker, stage-dependent melt-power floor, multi-stage tap-to-tap profile) embedded by the same procedure. The EAF setpoint gain is then extracted in closed form—without Jacobian inversion—as a ratio of first-order PCE coefficients, so it inherits the dispatch’s 95% feeder-security guarantee. Calibrated on 24 months of real wind/PV data for a Qingdao site (ERA5 reanalysis vs. archived ECMWF-IFS forecast), which confirms the heteroscedastic premise and a measured wind–PV error correlation of 0.015, the extracted gain scales across the Low–Mid–High zones (medians 6.07, 11.91, 17.22 p.u.) following the operating regime rather than the disturbance magnitude. The scheme bounds worst-case tracking below 1.18 MW per zone (vs. up to 3.34 MW for no droop), satisfies the feeder limit in 100% of realisations, matches model-predictive control without online optimisation, and lowers within-EAF specific CO₂ emissions by 4.4% versus no droop. An out-of-sample test on real records confirms a decisive advantage in the data-rich zones and, candidly, a shortfall in the data-limited high-wind zone. Full article

Renewable energy communities (RECs) are increasingly recognized as a strategic instrument for enhancing the sustainability and resilience of energy systems, promoting local renewable integration, and reducing consumer exposure to electricity market volatility. This study analyzes the Italian electricity market and assesses the economic performance of RECs relative to individual consumers using high-frequency hourly data from 2021 to 2023, covering both the 2022 European energy crisis and the subsequent Italian regulatory reform of incentive mechanisms. The optimization problem is formulated in physical terms, aiming to maximize locally utilized energy, defined as the sum of self-consumed and shared photovoltaic generation. This choice reflects the structure of the Italian regulatory framework, where incentives are directly linked to the amount of energy shared within the community. In this context, energy-based optimization is preferred to avoid embedding assumptions on discount rates, investment horizons, and financing conditions, which may vary significantly across users and introduce additional uncertainty. From a sustainability perspective, maximizing local energy utilization contributes to improving energy efficiency, reducing reliance on external energy sources, and enhancing the capacity of decentralized systems to absorb market shocks. For this reason, economic indicators such as Net Present Value (NPV) or payback period are not explicitly included in the optimization objective. This is justified by the focus of the analysis on short-term operational performance and exposure to electricity price volatility, rather than long-term investment evaluation. Moreover, given that the economic value of the REC is largely determined by shared energy volumes under the current Italian incentive scheme, maximizing local energy utilization provides a consistent proxy for economic performance. Nevertheless, the integration of financial metrics such as NPV or payback period represents a relevant extension for future research, particularly in the context of investment decision-making. Through panel econometric analysis, we estimate the sensitivity of economic value to electricity price fluctuations. Results show that RECs reduce price sensitivity by approximately 8–15% compared to individual users, as estimated by panel regression coefficients. Furthermore, the volatility of economic value decreases by around 1.95% under the community configuration, particularly during the 2022 price shock demonstrating that RECs exhibit significantly lower price dependence than standalone consumers. To assess the robustness of these findings, a machine learning framework is employed to relax linearity assumptions and capture potential non-linear effects. Results consistently show that while market prices remain an important determinant, RECs substantially attenuate their impact, particularly during periods of extreme price stress. A policy counterfactual comparison between pre- and post-reform incentive structures further indicates that the coefficient of variation decreases by approximately 4.4% under the post-reform incentive scheme, highlighting the role of policy design in supporting economically and operationally sustainable energy communities. Overall, this study develops a data-driven analysis based on a high-frequency synthetic dataset designed to reproduce realistic consumption and generation dynamics, providing robust evidence that RECs contribute not only to renewable energy deployment but also to the economic and systemic sustainability of electricity markets under conditions of high volatility. Full article

Hydrological variability affects the operational sustainability of run-of-river hydroelectric plants, particularly in Andean basins with marked climatic seasonality. This study evaluated the effective water availability of the Ambi River basin in Ecuador for the La Algodonera and Atuntaqui hydroelectric plants, integrating hydroclimatic analysis, water balance, flow duration curves, ecological flow estimation using the Tennant method, and hydrological projections from the LSTM network. The results showed marked seasonality in the hydrological regime and allowed the identification of characteristic values of Q₂₀ = 6.98 m³/s, Q₅₀ = 4.29 m³/s and Q₉₅ = 0.98 m³/s, with Q₅₀ being adopted as the design flow because it represents a reliable average condition for the system. The incorporation of the ecological flow established thresholds of 0.471 m³/s in the dry season and 1.413 m³/s in the rainy season, demonstrating that gross water availability is not equivalent to effective availability for generation. Under current conditions, peak power reached 1.92 MW, and monthly energy production ranged between 1.2 and 1.4 GWh during the months of highest availability. The hydrological projection indicated future flows between 0.9 and 9.60 m³/s, while territorial suitability analysis identified favourable areas for future photovoltaic integration, broadening system planning under an integrated water–energy perspective. Full article