Search Results (516)

The increasing penetration of wind generation requires performance evaluation methods that extend beyond average annual energy production. Temporal delivery characteristics, such as monthly dispersion and exposure to low-production periods, can influence both technical robustness and economic sensitivity. Building upon a previously developed probabilistic and entropy-based assessment framework, this study evaluates the robustness of delivery-oriented performance metrics for onshore and offshore wind units under parametric and economic uncertainty. Using high-resolution operational data from four wind units (three onshore and one offshore), the analysis incorporates percentile sensitivity, threshold variation in low-production exposure, bootstrap-based uncertainty intervals, and Monte Carlo simulation of economic inputs including CAPEX, operation and maintenance costs, and discount rate. The results indicate that variations in percentile definitions and stochastic economic assumptions modify absolute performance values but do not substantially alter the relative positioning between offshore and onshore units. Averaged over 2022–2024, the analyzed offshore unit exhibited a lower monthly energy dispersion coefficient ($C{V}_E=0.255$) [Reviewer2]than the analyzed onshore units ($C{V}_E=0.368$), [Reviewer2]corresponding to an approximate 30% reduction in relative variability. The offshore unit also showed lower mean low-production exposure ($LPE=0.526$ versus $0.581$ for onshore units) [Reviewer2]and consistently lower amplification of robustness-adjusted LCOE under conservative delivery assumptions. These results indicate that the analyzed offshore unit retains stronger delivery robustness and lower economic sensitivity across the tested parameter ranges. The proposed robustness-validation framework complements conventional yield-based assessments and provides additional insight for risk-aware evaluation of wind generation assets in renewable-dominated power systems. Full article

This article proposes and validates a hybrid structural reinforcement strategy for onshore wind turbine foundations in repowering projects, enabling the installation of higher-capacity units without demolishing the existing foundation. In a context of increasing demand for renewable energy and infrastructure optimization, the original foundation is reused as the primary element for global stability and serviceability limit state (SLS) requirements, while ultimate limit state (ULS) demands, arising from the replacement of approximately 1.5 MW turbines with 4.1 MW and 6.25 MW units with power ratings representative of various manufacturers’ models in the current market are resisted by a new peripheral reinforced concrete strengthening system. The study considers both shallow (gravity) and piled foundation typologies, which are the most common globally for wind turbines. This solution, applied to a commercially operating wind farm in southern Brazil with actual load data, demonstrated a substantial reduction in concrete volume–up to 80% for shallow foundations and 40% for piled foundations compared to constructing an entirely new foundation. Structural assessment was performed through numerical modeling in SAP2000, employing a shell-beam hybrid model validated against a 3D solid reference, combined with analytical verifications of limit states. Results confirm that the proposed solution ensures global serviceability and adequate ultimate limit state capacity, achieving significant material optimization. This offers a sustainable and efficient alternative for repowering wind turbine foundations, with notable economic and environmental benefits, including the elimination of demolition, transportation, and material disposal costs. Full article

This study presents a structural and geotechnical assessment of an onshore wind turbine foundation that has been in service for approximately 15 years. It aimed to evaluate its suitability for service life extension under the current operational conditions, within the broader context of decision-making in aging wind farms. The investigation integrated original design documentation, detailed field inspections, in situ and laboratory geotechnical testing, and advanced 3D numerical modeling incorporating soil–structure interaction effects. Verification procedures followed international standards and current guidelines for the design and reassessment of wind turbine foundations. Critical structural and geotechnical aspects, including internal forces and reinforcement demand, stiffness, bearing resistance, settlement, and global stability, are examined to verify performance under the current operational loading conditions. The results provide a sound technical basis for strategic decision-making regarding service life extension or decommissioning of wind turbines in established wind farms, and constitute an essential baseline for any future structural upgrading associated with repowering strategies. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

Extreme hydrometeorological events are occurring more often under climate change, increasing the risk for cities in coastal zones and lower river reaches. Such areas are prone to compound flooding (CF), where flood duration and magnitude are amplified by the combined effects of storm surges, onshore winds, long-term sea-level rise, and increasingly frequent rainfall-driven floods. This study assesses the socio-economic risk of residential neighbourhoods (RNs) along the lower reach of the Danė River in the city of Klaipėda, Lithuania, using a composite socio-economic risk index (CSERI) developed in this study under an extreme flood scenario, if the sea level in the south-eastern Baltic Sea rises by 1 m by the end of the century. The results show a strong relationship between water levels in the Klaipėda Strait and the lower reach of the Danė River, confirming a CF regime, where flood magnitude is driven by the interaction between strait water level and river discharge. The CSERI is based on five risk sub-indices (SIs): the building risk SI, road infrastructure risk SI, population risk SI, economic entities risk SI, and cultural heritage risk SI. The assessment identifies RNs at greatest risk under climate change and anthropogenic pressure and indicates priority areas for adaptation measures to reduce potential socio-economic losses. The proposed CSERI provides a practical decision-support tool for sustainable and climate-resilient urban development in coastal cities. Full article

Many countries are actively promoting the large-scale deployment of wind power generation, both onshore and offshore. However, damage to wind turbines caused by winter lightning has become a growing concern in Japan. Japan has made efforts since an early stage to establish legal frameworks for reducing lightning damage; nevertheless, lightning damage to wind turbines remains a problem that has not been completely eradicated. After a wind turbine has been struck by lightning, it is restarted only after its structural integrity has been verified; however, the current method relies on visual inspection by workers, making accurate and rapid inspections difficult. One approach to solving this problem is to use anomaly detection techniques based on SCADA data. Research is currently underway to implement this approach. However, anomaly detection methods based on SCADA data have been criticized for their limited ability to accommodate multiple operating modes, including de-rated operation. In this study, we propose the “scaled power curve” as a robust feature that is less affected by operating modes, with its effectiveness verified through anomaly detection. This method showed improved anomaly detection accuracy compared to using the original power curve as a feature; moreover, in the present case, the method remained effective under de-rated operation. By using this feature, it is expected that a lightning damage detection model can be developed, contributing to improved availability of wind turbines. Full article

The offshore wind energy sector requires efficient logistics to retrieve generated electricity using maritime mobile energy storage systems. This study addresses the maritime mobile energy storage scheduling problem to maximise the total net energy delivered to the onshore grid. The proposed approach utilises a mixed-integer linear programming framework. The mathematical formulation integrates a replicated port node mechanism to plan multi-trip operations over a continuous planning horizon. Additionally, the model accounts for energy transfer loss coefficients and incorporates a speed discretisation strategy to balance propulsion consumption against retrieved electricity. Numerical experiments based on simulated operational scenarios demonstrate the effectiveness of this method. The results indicate that expanding vessel storage capacity from 500 to 600 megawatt-hours eliminates the necessity for multi-stop trips, thereby reducing propulsion energy consumption from 270.79 to 73.65 megawatt-hours. Furthermore, increasing the fleet size from five to six vessels enables the full retrieval of available offshore electricity while decreasing fleet propulsion consumption to 91.08 megawatt-hours. The solver consistently achieves optimal solutions within an average of 0.88 s. Consequently, this framework provides operators with precise decision support for determining fleet capacity and configuring offshore energy retrieval networks. Full article

Simple trigger logic is commonly used in actual wind farms to monitor unit conditions, which face problems such as a high false-alarm rate and overlapping alarms. In addition, the characteristics of SCADA data, such as large quantity, complexity, and variable correlation, lead to insufficient accuracy of fault diagnosis. To address this problem, an improved fault diagnosis method based on a Multi-Channel Attention Mechanism Convolutional Neural Network (MCAMCNN) is proposed. Firstly, feature analysis is performed after preprocessing SCADA data to fully explore the coupling characteristics between data, and a dataset is established. Then, the proposed fault diagnosis model is used for feature screening. Innovatively, a structure combining double-layer multi-scale convolution and multi-channel attention is adopted to extract multi-domain features and dynamically calibrate the weights of feature channels. Fault classification is realized after adaptive fusion of features by Efficient Channel Attention (ECA). Finally, experiments are designed based on real data from an onshore wind farm in China, which verify that the method is timely and accurate in fault diagnosis, with significantly improved accuracy and F1-score, and has obvious advantages over comparative methods. Full article

The unit costs of power generation of onshore wind and photovoltaics in China have dropped rapidly and significantly since 2010. Recent studies have indicated that the learning effect on cost reduction could have been overestimated due to the exclusion of the equipment-level installed capacity and the price of capital. To address this estimation bias, we constructed a research framework comprising a one-factor analysis model (OFAM), a two-factor analysis model (OFAM), and a multi-factor analysis model (MFAM) based on the Cobb–Douglas function and the cost minimization problem. This framework examines the determinants of unit costs in renewable energy generation in consideration of learning effects, scale effects, and price effects. This paper uses data from institutions such as IRENA and the World Bank to empirically analyze the contributions of these factors to reductions in the cost of onshore wind and photovoltaic power generation in China from 2010 to 2022. The results indicate that the learning-by-doing (LBD) effect has been overestimated, with scale effects accounting for a significant portion of the cost reduction. Moreover, the price of capital exerts a more pronounced influence on the levelized cost of electricity (LCOE) for photovoltaics. After factoring in equipment scale and capital costs, LBD continues to significantly reduce the LCOE of photovoltaics, with the LBD learning rate declining from 23.85% to 6.30%. Meanwhile, the impact of LBD on the LCOE of onshore wind technology ceases to be significant. Both technologies exhibit economies of scale, with scale effects accounting for 41.60% and 34.12% of the LCOE reductions for onshore wind and photovoltaics, respectively. Capital costs accounted for 32.50% of the LCOE reduction for photovoltaics. Therefore, future large-scale deployments of other costly renewable energy technologies may also benefit from the equipment-level scale and favorable bank interest rates in addition to learning-by-doing. Full article

Life cycle greenhouse gas (GHG) accounting is increasingly required to substantiate the climate value of wind power beyond “zero-emission” operation, especially under China’s dual-carbon targets. Robust estimation of life cycle GHG emission intensity and the identification of actionable mitigation levers are therefore important for credible transition planning. In this study, a process-based life cycle assessment (LCA) was conducted for a representative 100 MW onshore wind farm in Gaoyou, Jiangsu Province, China, following ISO 14040/14044. To enhance engineering relevance, the construction and installation phase was modeled in a refined manner by decomposing it into road, wind-turbine, booster-station, and transmission-line engineering and further into unit processes. The results show that the overall life cycle GHG emission intensity of the studied wind farm is 24.6 g CO₂-eq/kWh. Scenario analysis further indicates that reducing curtailment and improving end-of-life recycling are effective pathways to lower emission intensity, while the net advantage of hybrid versus steel towers depends on recycling performance when end-of-life credits are included. The study also summarizes practical implications for low-carbon equipment/material procurement and green supply-chain governance, low-carbon construction and logistics, coordinated “source–grid–load–storage” planning to curb curtailment, and more standardized and comparable life cycle carbon accounting for wind projects in China. Full article

Green ammonia is increasingly recognised as a sustainability enabler for decarbonising fertiliser production, energy storage, and maritime transport, but offshore wind-to-ammonia pathways remain subject to significant economic and operational uncertainty. This study evaluated the techno-economic and sustainability performance of integrating power-to-ammonia (PtA) with an operating offshore wind farm in Denmark under three supply-chain scenarios (SCs): SC1, a fully offshore PtA with vessel-based ammonia transport; SC2, a fully offshore PtA with pipeline export; and SC3, a hybrid offshore–onshore configuration. An hourly dispatch framework allocated wind electricity between grid export and ammonia production by comparing incremental operating margins, while accounting for minimum-load, ramping, storage, and logistics constraints. Hourly wind generation and DK1 electricity-price data for 2020–2025 are used to construct a deterministic base case and a 30-year block-bootstrap Monte Carlo analysis. Sensitivity analysis is performed by varying electrolyser rated power over 10–200 MW and ammonia selling price over 1400–3200 €/tNH3, with additional breakeven-price estimation and flexibility cases based on reduced minimum-load requirements and faster ramping. A screening-level climate indicator was additionally reported by estimating potential CO₂ emissions avoided if delivered green ammonia displaces conventional natural-gas-based ammonia. Results indicated that SC3 is the most favourable configuration under the adopted assumptions, while overall project viability remained highly sensitive to PtA sizing, ammonia market value, operational flexibility, and the assumed infrastructure cost structure. Full article

This study compares the performance of two wind farm sites located in Northern Europe: an onshore site and an offshore area in the eastern Baltic Sea region. This study investigates the optimisation of wind farm performance within a fixed project area by maximising annual energy production (AEP) and increasing energy density. Three wake-loss scenarios (≤10%, ≤15%, and ≤20%) were examined to assess the sensitivity of layout optimisation to aerodynamic interaction constraints. Several layout configurations were analysed to reduce wake losses and enhance overall energy output. Wind conditions were assessed using NORA3 reanalysis data, and wake interactions were modelled using the Jensen wake model to estimate AEP. Both wind farms were further compared across key criteria, including cost, power generation efficiency, installation and maintenance requirements, and site availability. Offshore wind farms achieve 1.5–1.7 times higher energy density under similar spatial conditions. However, offshore levelised cost of energy (LCOE) remains roughly 25% higher due to higher capital and infrastructure costs, while onshore LCOE demonstrates better economic performance, driven by lower CAPEX and O&M expenses. The findings highlight the trade-offs between cost efficiency and wake-driven energy performance for onshore and offshore wind development in the eastern Baltic Sea region. Full article

Renewable energy sources (RES) will be the main source of energy in the future. The main goal of this study was to analyze and elaborate a prognosis for the development of renewable energy sources in Poland. Specific objectives included: evaluation of the prognosis developed as part of Poland’s energy policy (PEP), development of our own forecast of the share of renewable energy sources, and comparison of the forecast developed for Poland’s energy policy with our own forecast. We have also elaborated a hypothesis that the prognosis for the development of renewable energy sources for Poland prepared by PEP, and our own prognosis based on Autoregressive Moving Average (ARIMA) models, are both promising and confirm the development of the renewable energy sector in the future. We used the Augmented Dickey–Fuller (ADF) test as well as ARIMA models. Moreover, we compared own RES prognosis with prognoses proposed by the European Union. Cumulative capital expenditures from 2021 to 2040, including financing costs, will amount to PLN 300 billion, of which PLN 195 billion go towards renewable energy sources alone. Photovoltaics (PV) will account for the largest share of energy production, reaching 16 GW of achievable capacity, followed by onshore wind farms with 9.7 GW. Solid biomass accounts for the largest share of renewable energy consumption in heating and cooling, although its share is gradually decreasing from 98.6% in 2005 to a projected 75% in 2040. Heat pumps, which had no share in 2005, are expected to increase their share to a projected 11.8% in 2040. Solar technology has also increased from 0% in 2005 to a projected 5.6% in 2040. The share of renewable energy in this energy sector is increasing from 22.1% in 2020 to 31.8% in 2030 and 39.7% in 2040. The prognosis elaborated by PEP for 2025–2040 are very optimistic and the prognosis elaborated based on ARIMA models is also promising. Both prognoses predict the development of RES in the future and the transformation of the energy sector in Poland. Full article

The accelerated decarbonization agenda of the European Union, supported by the European Green Deal, the Fit for 55 package, and REPowerEU, increases pressure on member states to reduce dependence on fossil fuels and expand renewable generation. Poland, whose power sector remains strongly coal-dependent, faces one of the most challenging and capital-intensive transition pathways in the EU. This study provides a comprehensive assessment of the costs and economic viability of Poland’s energy transition, focusing on the feasibility of replacing coal-based electricity generation with renewable technologies. The analysis applies three financial evaluation methods: net present value (NPV), internal rate of return (IRR), and levelized cost of electricity (LCOE). These tools are used to estimate investment costs of selected renewable technologies, assess the potential for coal substitution in the energy mix, and determine the profitability of renewable projects under selected scenarios. The results show that onshore wind power demonstrates the most favorable investment parameters, including the lowest LCOE and the shortest payback period, while photovoltaics exhibit lower profitability in the analyzed conditions. Nuclear energy may serve as a complementary stable source to variable renewables. The findings provide evidence-based insights supporting national energy planning and the design of future policy instruments. Full article

Accurate wind resource assessment is critical for the effective planning of wind farms, as well as for forecasting production values to ensure grid stability, yet it remains a complex challenge. This study evaluates the robustness of the Norwegian reanalysis model (NORA3) as a wind assessment tool specifically for the Baltic Sea region. The NORA3 model was validated by comparing it to observation data from four onshore locations in Latvia, collected from meteorological masts and a lidar wind measurement device. The evaluation applied correlation analysis, wind distribution and wind rose comparisons, and annual energy production (AEP) estimates. Results reveal high similarity between NORA3 and observation datasets in terms of wind speed correlation and distribution, while wind roses feature significant differences, especially for short-term observations. AEP estimates based on the NORA3 dataset are more optimistic compared to the actual observations for all investigated locations. Full article