Search Results (279)

The integration of large-scale wind power clusters significantly reduces the inertia level of the power system, increasing the risk of frequency instability. Accurately assessing the equivalent virtual inertia of wind farms is critical for grid stability. Addressing the dual bottlenecks in existing inertia assessment methods, where physics-based modeling requires full control transparency and data-driven approaches lack interpretability for inertia response analysis, thus failing to reconcile commercial confidentiality constraints with analytical needs, this paper proposes a symbolic regression framework for inertia evaluation in doubly fed wind farms with limited control information constraints. First, a dynamic model for the inertia response of DFIG wind farms is established, and a mathematical expression for the equivalent virtual inertia time constant under different control strategies is derived. Based on this, a nonlinear function library reflecting frequency-active power dynamic is constructed, and a symbolic regression model representing the system’s inertia response characteristics is established by correlating operational data. Then, sparse relaxation optimization is applied to identify unknown parameters, allowing for the quantification of the wind farm’s equivalent virtual inertia. Finally, the effectiveness of the proposed method is validated in an IEEE three-machine nine-bus system containing a doubly fed wind power cluster. Case studies show that the proposed method can fully utilize prior model knowledge and operational data to accurately assess the system’s inertia level with low computational complexity. Full article

Our regression analysis documents that energy policies to promote renewable energy development, as well as hydroelectric imports from Canada, lead to short-run reductions in average electricity prices (also known as merit-order effects) throughout the Northeast United States. Changes in the reliance upon renewable energy in one of the Northeast’s three interconnected electricity markets will impact wholesale prices in the other two. The retirement of a 1000 MW nuclear plant can increase prices by about 9% in the Independent System Operator of New England market and 7% in the New York Independent System Operator market in the short run at reference hubs, while also raising prices in neighboring markets. Some proposed large-scale off-shore wind farms would not only lower prices in local markets at the reference hubs modeled but would also lower prices in neighboring markets. Full article

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method. Full article

After primary frequency regulation in large-scale wind farms is completed, the power dip phenomenon occurs during the rotor speed recovery phase. This phenomenon may induce a secondary frequency drop in power systems, which poses challenges to system frequency security. To address this issue, this paper proposes a frequency security-oriented optimal dispatch model for multi-regional power systems, taking into account the risks of secondary frequency drop. In the first stage, risk-averse day-ahead scheduling is conducted. It co-optimizes operational costs and risks under wind power uncertainty through stochastic programming. In the second stage, frequency security verification is carried out. The proposed dispatch scheme is validated against multi-regional frequency dynamic constraints under extreme wind scenarios. These two stages work in tandem to comprehensively address the frequency security issues related to wind power integration. The model innovatively decomposes system reserve power into three distinct components: wind fluctuation reserve, power dip reserve, and contingency reserve. This decomposition enables coordinated optimization between absorbing power oscillations during wind turbine speed recovery and satisfies multi-regional grid frequency security constraints. The column and constraint generation algorithm is employed to solve this two-stage optimization problem. Case studies demonstrate that the proposed model effectively mitigates frequency security risks caused by wind turbines’ operational state transitions after primary frequency regulation, while maintaining economic efficiency. The methodology provides theoretical support for the secure integration of high-penetration renewable energy in modern multi-regional power systems. Full article

The wind energy resources in the northwestern desert and semi-desert grassland regions of China are abundant. However, the ramifications of large-scale centralized wind farm operations on terrestrial rodents remain incompletely understood. In May and September 2024, we employed a grid sampling method combined with burrow counting and kernel density analysis to investigate the spatial distribution of Alashan ground squirrel (Spermophilus alashanicus) burrows in different wind turbine power zones (control, 750 kW, 1500 kW, 2000 kW, and 2500 kW) at the Taiyangshan wind farm in China. Using generalized additive models and structural equation models, we analysed the relationship between burrow spatial distribution and environmental factors. The results revealed no significant linear correlation between burrow density and turbine layout density, but was significantly positively correlated with turbine power (p < 0.05). The highest burrow density was observed in the 2500 kW zone, with values of 24.43 ± 7.18 burrows/hm² in May and 21.29 ± 3.38 burrows/hm² in September (p < 0.05). The squirrels exhibited a tendency to avoid constructing burrows within the rotor sweeping areas of the turbines. The burrow density distribution exhibited a multinuclear clustering pattern in both May and September, with a northwest–southeast spatial orientation. Turbine power, aspect, and plan convexity had significant positive effects on burrow density, whereas vegetation height had a significant negative effect. Moreover, vegetation height indirectly influenced burrow density through its interactions with turbine power and relief degree. Under the combined influence of turbine power, topography, and vegetation, Alashan ground squirrels preferred habitats in low-density, high-power turbine zones with shorter vegetation, sunny slopes, convex landforms, and minimal disturbance. Full article

Although wind turbine installation vessels (WTIVs) are increasingly operating in deepwater complex geological areas with larger scales, systematic research on and experimental validation of platform jack-up stability remain insufficient. This study aimed to establish a comprehensive evaluation framework encompassing penetration depth, anti-overturning/sliding stability, and punch-through risk, thereby filling the gap in holistic platform stability analysis. An entire four-legged centrifuge test at 150× g was integrated with coupled Eulerian–Lagrangian (CEL) numerical simulations and theoretical methods to systematically investigate spudcan penetration mechanisms and global sliding/overturning evolution in clay/sand. The key findings reveal that soil properties critically influence penetration resistance and platform stability: Sand exhibited a six-times-higher ultimate bearing capacity than clay, yet its failure zone was 42% smaller. The sliding resistance in sand was 2–5 times greater than in clay, while the overturning behavior diverged significantly. Although the horizontal loads in clay were only 50% of those in sand, the tilt angles at equivalent sliding distances reached 8–10 times higher. Field validation at Guangdong Lemen Wind Farm confirmed the method’s reliability: penetration prediction errors of <5% and soil backflow/plugging effects were identified as critical control factors for punch-through risk assessment. Notably, the overturning safety factors for crane operation at 90° outreach and storm survival were equivalent, indicating operational load combinations dominate overturning risks. These results provide a theoretical and decision-making basis for the safe operation of large WTIVs, particularly applicable to engineering practices in complex stratified seabed areas. Full article

Timely and accurate detection of wind turbine blade surface defects is crucial for ensuring operational safety and improving maintenance efficiency with respect to large-scale wind farms. However, existing methods often suffer from poor generalization, background interference, and inadequate real-time performance. To overcome these limitations, we developed an end-to-end defect recognition framework, structured as a three-stage process: blade localization using YOLOv5, robust feature extraction via the large vision model DINOv2, and defect classification using a Stochastic Configuration Network (SCN). Unlike conventional CNN-based approaches, the use of DINOv2 significantly improves the capability for representation under complex textures. The experimental results reveal that the proposed method achieved a classification accuracy of 97.8% and an average inference time of 19.65 ms per image, satisfying real-time requirements. Compared to traditional methods, this framework provides a more scalable, accurate, and efficient solution for the intelligent inspection and maintenance of wind turbine blades. Full article

The growing scale of offshore wind farms and increasing transmission distances has driven the demand for optimized offshore substation (OSS) configurations. This study proposes a comprehensive techno-economic framework to minimize the total lifecycle cost (LCC) of an OSS by determining the optimal number of OSSs and transformers considering wind farm capacity and transmission distance. The methodology incorporates three cost models: capital expenditure (CAPEX), operational expenditure (OPEX), and expected energy not supplied (EENS). CAPEX considers transformer costs, topside structural mass effects, and nonlinear installation costs. OPEX accounts for substation maintenance and vessel operating expenses, and EENS is calculated using transformer failure probability models and redundancy configurations. The optimization is performed through scenario-based simulations and a net present value (NPV)-based comparative analysis to determine the cost-effective configurations. The quantitative analysis demonstrates that for small- to medium-scale wind farms (500–1000 MW), configurations using 1–2 substations and 3–4 transformers achieve minimal LCC regardless of the transmission distance. In contrast, large-scale wind farms (≥1500 MW) require additional substations to mitigate transmission losses and disruption risks, particularly over long distances. These results demonstrate that OSS design should holistically balance initial investment costs, operational reliability, and supply security, providing practical insights for cost-effective planning of next-generation offshore wind projects. Full article

In the face of escalating climate challenges, the energy sector is increasingly investing in renewable projects. However, the implementation of utility-scale renewable energy initiatives is often hindered by public opposition. While previous research has offered detailed insights into the determinants of public acceptance generally, there remains scope for examining the impact of strategies that developers can employ to coalesce support for success at the local level. This study contributes with a comparative case study of two U.S. utility-scale projects: the Empire Wind offshore wind farm, which achieved success through proactive conflict resolution, and the Northern Pass hydroelectric transmission project, which was halted due to sustained local resistance. Our findings suggest the critical importance of community-centered conflict resolution strategies in garnering public support and facilitating the successful deployment of large-scale renewable energy projects. Full article

The transportation of wind turbine blades in remote wind farm areas poses significant safety risks to both personnel and infrastructure. These risks arise from collision hazards, complex terrain, and the difficulty of real-time monitoring under adverse environmental conditions. To address these challenges, this study proposes an intelligent safety monitoring framework that combines machine vision with edge–cloud collaboration. The framework employs an optimized YOLOv7-Tiny model. It is enhanced with convolutional block attention modules (CBAMs) for feature refinement, CARAFE upsampling for better contextual detail, and bidirectional feature pyramid networks (BiFPNs) for multi-scale object detection. The system was validated at the Lingbi Wind Farm in China. It achieved over 95% precision in detecting safety violations, such as unauthorized vehicle intrusions and personnel proximity violations within 2 m, while operating at 48 frames per second. The edge–cloud architecture reduces latency by 30% compared to centralized systems. It enables alert generation within 150 milliseconds. Dynamic risk heatmaps derived from real-time data help reduce collision probability by 42% in high-risk zones. Enhanced spatial resolution further minimizes false alarms in mountainous areas with poor signal conditions. Overall, these improvements reduce operational downtime by 25% and lower maintenance costs by 18% through proactive hazard mitigation. The proposed framework provides a scalable and energy-efficient solution for safety enhancement in renewable energy logistics. It balances computational performance with flexible deployment and addresses key gaps in intelligent monitoring for large-scale wind energy projects. This work offers valuable insights for sustainable infrastructure management. Full article

Renewable energy production is one of the pillars of the decarbonization process for the electricity system. The use of hydrogen can also contribute to the decarbonisation of industrial sectors such as chemicals, steel production, heavy industry, and long-distance transports. In Italy, a significant growth in wind and photovoltaic production is already foreseen by 2030. After that date, a wide deployment of offshore wind is expected with a significant decrease in cost. In a medium-long term scenario, with the massive expansion of renewable energy systems and the growing demand for hydrogen across multiple sectors, it is conceivable that some large-scale offshore wind farms (OWFs) could be exclusively dedicated to on-site green hydrogen production, thereby mitigating the impact on the electrical grid and simultaneously increasing hydrogen availability. This study reports the methods, assumptions, and results of a technical–economic analysis carried out for green hydrogen production from dedicated OWFs in two Italian offshore sites, one in Sicily and one in the Adriatic Sea. Despite the high uncertainty associated with carrying out this type of assessment for emerging technologies, the levelized costs obtained for dedicated offshore wind energy (approximately 70–80 EUR/MWh) and green hydrogen (approximately 5–6 EUR/kg) are in line with corresponding sector studies. Moreover, the simplified methodological approach developed is useful to analyse and compare other marine areas and different system configurations. Full article

In Europe, renewable energy sources such as photovoltaic panels and wind power plants are developing dynamically. The growth of renewable energy is driven by rising energy prices, greenhouse gas emission restrictions, the European Union’s Green Deal policy, and decarbonization efforts. Photovoltaic farms generate energy intermittently, depending on weather conditions. Given the increasing number of new installations, ensuring the power balance and transmission capacity of the electrical grid has become a major challenge. To address this issue, the authors propose a technical solution that allows the energy generated by photovoltaic systems to be stored in the form of heat. Thermal energy from solar power and wind energy offers significant potential for energy storage. It can be accumulated during summer in specially designed sand-based heat storage systems and then used for heating purposes in winter. This approach not only reduces heating costs but also decreases greenhouse gas emissions and helps balance the power grid during sunny periods. Post-industrial areas, often located near city centers, are suitable locations for large-scale heat storage facilities supplying, among others, public utility buildings. Therefore, this article presents a concept for utilizing high-temperature sand-based heat storage systems built in decommissioned underground mining excavations. Full article

Floating Offshore Wind Turbines (FOWTs) are gaining traction as a solution for harnessing wind energy in deepwater regions where traditional fixed-bottom turbines may not be viable due to water depth. This paper investigates the feasibility and optimization of a floating wind farm in a tropical cyclone (typhoon) region, using the IEA 15 MW turbine and semi-submersible floaters. Because of the extreme environment, the FOWT’s mooring system requires nine catenary chains in a 3 × 3 pattern, which has a large footprint. One challenge in the wind farm design is fitting the FOWTs in a limited area and minimizing wake effects. This research compares a linear layout and an offset grid layout, focusing on the effects of spacing and wake dynamics. The results show that while the linear layout maintains optimal power generation without energy loss, the offset grid layout, although resulting in 2% energy loss, offers greater spatial efficiency for larger-scale projects. The findings highlight the importance of balancing energy efficiency with spatial optimization, particularly for large offshore wind farms. This study explores the use of the Gauss–Curl hybrid model in wake modeling, and the methodology employed provides insights into FOWT placement and mooring system arrangement. The result concludes that a wind farm containing twelve (12) units of 15 MW wind turbines can achieve the 7.0 MW/km² power generation density required by a regulatory government agency. It proves the technical feasibility of a wind farm congested with large mooring systems in a tropical cyclone region. Full article

Taiwan has been actively advancing offshore wind energy, with significant progress in deep-sea and large-scale turbine development. However, this growth poses challenges to coastal fishery communities, particularly regarding the protection of fishery rights and livelihoods. This study employs the Life Cycle Sustainability Assessment (LCSA) framework to evaluate the impact of offshore wind farm (OWF) on fishery rights in Taiwan. Through an extensive literature review, we identify key indicators influencing fishery rights within the OWF context. To ensure a comprehensive analysis, expert surveys from diverse fields provide additional insights into these impacts. By aligning our findings with international frameworks, the International Finance Corporation (IFC) Performance Standards (PS) and the Equator Principles (EP), this research underscores the significance of integrating both local concerns and global standards in OWF development. In the lifecycle of long-term, large-scale OWF projects, PS1 of the IFC PS is the most widely applicable standard, whereas P2, P4, P5 and P9 of the EP plays a central role in ensuring compliance and operational efficiency. This study uniquely integrates local fishery rights into global frameworks, bridging regional socio-economic concerns with international sustainability standards—a novel approach to balancing offshore wind development with community interests. Ultimately, this research emphasizes the importance of balancing renewable energy advancement with the preservation of fishery rights. Full article

Since the 1990s, Polish energy companies have been using new technologies to build wind farms, consisting of large devices. Over the years, the power and the size of installations have increased, and it continues to do so. In Poland, as well as in other countries, a problem with the post-use management of wind turbine blades has appeared. The recycling of wind turbine blades has remained challenging hitherto. The utilization of many different materials and changes in the dimensions cause multi-material waste. Since there are no economically viable recycling technologies available for such large-scale composite products, other treatment strategies for disposed WTBs have to be considered. This study explores the repurposing of WTBs as a pro-environmental alternative approach from a technological and architectural point of view. For this purpose, the study is guided by an analysis of wind turbine locations in reference to the impending need for waste management of wind blades in Poland. Well-profiled blades help transfer a large portion of wind energy to turbine rotors, which is why their construction is a challenge when it comes to designing new objects or elements thereof from decommissioned blades. They have a continuous curvature, where both the cross-section and thickness change, which is why, in the design of architectural or engineering objects, they are cut into smaller parts. This solution makes it possible to optimize the load-bearing properties of individual segments, ensuring a more stable system. Smaller elements also provide greater freedom in shaping architectural forms, which is associated with better control of the final effect from the aesthetic side. The potential of repurposing WTBs is shown, for example, in the design concept for the Archery Centre in Poznan (Poland). Full article