Wind Farm Research Articles

Application of Robust Optimization Algorithms to Wind Power Systems

In the current context of global energy structure transformation and climate change response, the development and utilisation of wind power, as a kind of clean and renewable energy, has received extensive attention. However, the intermittency and uncertainty associated with wind power present challenges to its stability and reliability in power system scheduling. This paper provides an in-depth analysis of the application of robust optimization algorithms in wind power systems, focusing on how these algorithms can enhance the efficiency and stability of wind power within power systems. This paper discusses the specific methods of robust optimisation algorithms for solving problems in wind power systems from various perspectives. For example, by constructing a robust optimisation model that takes into account the uncertainty of wind power output, the scheduling flexibility and economy of the wind power system can be effectively improved. At the same time, the application of robust optimisation algorithms is analysed in terms of improving the anti-interference capability of wind power systems, optimising the combination of wind turbines, grid integration and wind farm planning, and improving the accuracy of wind power forecasts. The effectiveness of the algorithms in solving the problems encountered in the scheduling of wind power systems is analysed, and the challenges and opportunities for future development are discussed.

Simulations suggest offshore wind farms modify low-level jets

Abstract. Offshore wind farms are scheduled to be constructed along the East Coast of the US in the coming years. Low-level jets (LLJs) – layers of relatively fast winds at low altitudes – also occur frequently in this region. Because LLJs provide considerable wind resources, it is important to understand how LLJs might change with turbine construction. LLJs also influence moisture and pollution transport; thus, the effects of wind farms on LLJs could also affect the region’s meteorology. In the absence of observations or significant wind farm construction as yet, we compare 1 year of simulations from the Weather Research and Forecasting (WRF) model with and without wind farms incorporated, focusing on locations chosen by their proximity to future wind development areas. We develop and present an algorithm to detect LLJs at each hour of the year at each of these locations. We validate the algorithm to the extent possible by comparing LLJs identified by lidar, constrained to the lowest 200 m, to WRF simulations of these very low LLJs (vLLJs). In the NOW-WAKES simulation data set, we find offshore LLJs in this region occur about 25 % of the time, most frequently at night, in the spring and summer months, in stably stratified conditions, and when a southwesterly wind is blowing. LLJ wind speed maxima range from 10 m s−1 to over 40 m s−1. The altitude of maximum wind speed, or the jet “nose”, is typically 300 m above the surface, above the height of most profiling lidars, although several hours of vLLJs occur in each month in the data set. The diurnal cycle for vLLJs is less pronounced than for all LLJs. Wind farms erode LLJs, as LLJs occur less frequently (19 %–20 % of hours) in the wind farm simulations than in the no-wind-farm (NWF) simulation (25 % of hours). When LLJs do occur in the simulation with wind farms, their noses are higher than in the NWF simulation: the LLJ nose has a mean altitude near 300 m for the NWF jets, but that nose height moves higher in the presence of wind farms, to a mean altitude near 400 m. Rotor region (30–250 m) wind veer is reduced across almost all months of the year in the wind farm simulations, while rotor region wind shear is similar in both simulations.

Dynamic inverter station current control of HVDC system integrated offshore DFIG wind farm under onshore grid voltage distortions

A power control method for an HVDC system integrating a DFIG-based offshore wind farm supplying a weak onshore grid is the prime focus of this work. The real power generated by the wind farm is regulated by the LCC-based rectifier station and transmitted to the VSC-based inverter station through the HVDC line. The work proposes a control mechanism to integrate these two stations with better power quality management. The inverter station facilitates the effective transmission of generated power to the onshore grid under nominal onshore grid voltages. Nevertheless, the abnormalities in onshore grid voltage lead to converter failures due to huge harmonic grid currents and power pulsations beyond permissible limits; uninterrupted power transfer without compromising power quality is the target. The proposed method utilises a dynamic current control technique at the inverter station to inject compensating current. This enables independent regulation of active and reactive power, mitigating power oscillations and minimising grid current THD induced by onshore grid voltage deviations. The simulations in PSCAD/EMTDC and hardware-in-loop (HIL) experiments using OPAL-RT demonstrate that the proposed control strategy significantly reduces power oscillations while maintaining a grid current THD of 1.47% up to 50% unbalance in onshore grid voltage.

Building a blueprint for better wind farms

Model with analytic gradients to help inform turbine layout optimization

Overview of Offshore Wind Power Technologies

Optimizing offshore wind power technology and reducing the levelized cost of electricity throughout the lifecycle are key measures for the large-scale development of offshore wind power, contributing significantly to the transition toward sustainable energy systems. However, compared to onshore wind power, the internal flow dynamics of offshore wind farms are more complex, which poses challenges for operation and maintenance. Therefore, there is an urgent need for updated, smarter, more efficient, and economic offshore intelligent operation control technologies to facilitate the large-scale development and utilization of offshore wind power. This paper approaches the topic from two perspectives, offshore wind turbines and offshore wind farms, introducing popular research directions and technical bottlenecks in these two related fields. This includes offshore wind turbine capacity development and fundamental technologies, offshore wind power forecasting technology, and offshore wind power operation and control technology, offshore intelligent operation and maintenance technology, as well as offshore wind power and integrated marine area utilization technology. Firstly, the challenges faced by the intensive development of offshore wind resources and operational environments are analyzed. Secondly, the challenges encountered in the aforementioned technological areas and their potential solutions are summarized. Finally, a systematic reflection and outlook on the large-scale development of offshore wind power are provided, reinforcing its critical role in achieving global sustainability goals.

Assessing Impacts of Offshore Wind Development: An Analysis of the Minimization of Economic Exposure of the Scallop Fishery Through the Regulatory Process

ABSTRACTOffshore wind energy has expanded as a source of clean energy in the United States since the first US offshore wind farm began operations off the coast of Rhode Island in 2016. The emergence of offshore wind has increased the need to manage ocean use across multiple stakeholder groups, a difficult and contentious process. We use 15 years of scallop (Placopecten magellanicus) fishery data to describe how offshore wind may expose one of the most valuable commercial fisheries in the United States to economic risks. Our analysis shows that the current configuration of approved offshore wind lease areas off the northeastern coast of the United States is expected to result in relatively small economic exposure for the scallop fishery. We also illustrate how the measured development process, which includes ample opportunity for stakeholder input, has mitigated exposure through minimization or avoidance by characterizing the change in impacted activity through two case studies. We find moderate to strong levels of exposure mitigation across our three scallop fleet métiers within the Central Atlantic (CA) region. In contrast, exposure mitigation was more variable in the New York Bight (NYB) region suggesting mitigation methods in the NYB are not as effective for the scallop fishery as the CA. The open development process that allowed for early stakeholder engagement has largely mitigated the potential for economic risk of offshore wind on the scallop industry by approving the siting of offshore wind development in less utilized or less productive scalloping areas.

Study of Wind Power Prediction in ELM Based on Improved SSA

This paper proposes a short‐term wind power prediction model based on the improved Sparrow Search Algorithm (SSA) and Extreme Learning Machine(ELM) for anomalous wind power information from wind farms. The objective is to enhance the accuracy of short‐term wind power prediction. The model employs the extraction of features utilizing raw wind power history data from wind farms, in conjunction with the application of Variable Importance in Projection indices in Partial Least Squares (PLS‐VIP). As the ELM network model is susceptible to the influence of randomly generated input weights and thresholds at the outset of training, a solution is proposed whereby the input weights and thresholds of the ELM are optimized using SSA. The optimal weights and thresholds identified by SSA are then applied to the ELM model, thus forming the SSA‐ELM model. To address the limitations of traditional SSA, namely its susceptibility to local optimal solutions and poor global search ability, an improved SSA‐ELM algorithm is proposed. The improved SSA‐ELM algorithm introduces chaotic sequences and an exchange learning strategy to the original SSA. The rationale behind incorporating chaotic sequences is to enhance the quality of the initial solution, ensuring a more uniform distribution of sparrow positions and, consequently, a more diverse sparrow population. This, in turn, enables the algorithm to achieve a more effective global search capability through the utilization of the exchange learning strategy. Subsequently, all the data are fed into the SSA‐ELM model for prediction purposes. The simulation results demonstrate that the model exhibits enhanced prediction accuracy and improved practical applicability in wind power prediction. © 2025 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Christian Ocean Stewardship on the Taiwan Marine Wind Farm Policy and Cetacean Conservation

This study aims to explore the practice of Christian ocean stewardship on Taiwan’s marine wind farm policy, with a particular focus on the critically endangered Taiwanese humpback dolphins (Sousa chinensis taiwanensis). Marine wind farms, while integral to the shift toward renewable energy, present complex ethical challenges due to their adverse environmental impacts—particularly noise pollution, which poses a serious threat to vulnerable marine species. International laws have underscored the importance of preventing marine noise pollution. Although Taiwan has relevant laws and policies, their implementation and supervision in preventing marine noise pollution are inadequate. This study critically examines the anthropocentric frameworks that currently dominate Taiwan’s marine development policies, arguing that they inadequately address the moral obligations humans have toward the broader ecosystem. Through a theological reflection grounded in Christian stewardship ethics, this research advocates for a shift away from human-centered environmental policies towards a more holistic ethic that acknowledges the intrinsic value of all creation. It emphasizes that ethical stewardship requires not merely reducing harm but actively participating in the restoration and protection of ecosystems, thus extending beyond utilitarian considerations of human benefit. The plight of the Taiwanese humpback dolphin serves as a case study for exploring these ethical tensions, highlighting how the energy transition can inadvertently contribute to biodiversity loss if not approached with caution and moral responsibility. Building on this, this study proposed four key principles to guide future marine development. These principles advocate for respecting nature, responsible management, continuous innovation, and social participation and transparency. This approach not only helps guide Taiwan’s marine policies but also provides new perspectives and practical approaches for applying Christian ethics in the field of marine environmental protection.

An Adaptive Voltage Reference-Based Multi-Objective Optimal Control Method for the Power Flow Symmetry of Multi-Terminal DC Systems with the Large-Scale Integration of Offshore Wind Farms

The optimization of the symmetry of MTDC systems after a contingency is crucial for the stable and economic operation of the MTDC systems. In this paper, a multi-objective optimal control method for the power flow symmetry of MTDC systems for the large-scale integration of offshore wind farms is proposed. A mirror relationship between the available headroom of DC lines and VSCs and their actual power flow distribution performance is established. A corresponding symmetry index is established for the MTDC network, and the multi-objective optimization problem is converted into a series of single-objective problems by the normal boundary intersection method, and solved by the original dyadic interior point method, so as to obtain the Pareto optimal solution with uniform distribution. The compromise optimal solution is decided according to the entropy weight double-basis point method, which provides decision-making guidance for the operators. The simulation results show that the normal boundary intersection method can solve the multi-objective dynamic optimal control problem of the VSC-HVDC system quickly and efficiently, and improve the symmetry of the power flow in an MTDC network.

An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection

In recent years, clean energy has gained increasing attention, with offshore wind power playing a crucial role in global energy production. However, the high operating and maintenance costs of offshore wind farms remain a significant challenge. The advent of 5G technology provides a solution for efficiently monitoring and controlling wind power equipment. The use of 5G unmanned aerial vehicles (UAVs) for blade inspections is a promising development. A key challenge is efficiently planning UAV flight paths for fast and effective inspections in complex offshore environments. To address this problem, we conduct an in-depth study of the 5G UAV path optimization method. In this paper, the UAV inspection path problem is modeled as an obstacle avoidance traveling salesman problem (TSP), taking into full account UAV flight constraints and complex sea environment factors, particularly the impact of sea wind on UAV flight speed. We propose a novel Sea Wind-Aware Improved A*-Guided Genetic Algorithm (SWA-IAGA), which integrates an improved A* algorithm to guide the genetic algorithm for efficient path planning, with the assistance of relevant graphical knowledge. This algorithm overcomes the limitations of traditional single-path planning methods, enabling more accurate and efficient path planning.

Offshore Wind Power—Seawater Electrolysis—Salt Cavern Hydrogen Storage Coupling System: Potential and Challenges

Offshore wind power construction has seen significant development due to the high density of offshore wind energy and the minimal terrain restrictions for offshore wind farms. However, integrating this energy into the grid remains a challenge. The scientific community is increasingly focusing on hydrogen as a means to enhance the integration of these fluctuating renewable energy sources. This paper reviews the research on renewable energy power generation, water electrolysis for hydrogen production, and large-scale hydrogen storage. By integrating the latest advancements, we propose a system that couples offshore wind power generation, seawater electrolysis (SWE) for hydrogen production, and salt cavern hydrogen storage. This coupling system aims to address practical issues such as the grid integration of offshore wind power and large-scale hydrogen storage. Regarding the application potential of this coupling system, this paper details the advantages of developing renewable energy and hydrogen energy in Jiangsu using this system. While there are still some challenges in the application of this system, it undeniably offers a new pathway for coastal cities to advance renewable energy development and sets a new direction for hydrogen energy progress.

Site Suitability Analysis for Green Hydrogen Production Using Multi-Criteria Decision-Making Methods: A Case Study in The State of Ceará, Brazil

– Brazil is leveraging its extensive potential in solar, wind, and hydro energy to establish itself on the global map of green hydrogen production—a pivotal component in the ongoing energy transition. The economic viability of green hydrogen hinges on optimal locations with abundant renewable resources, space for solar or wind farms, access to water, and the potential for exporting to major demand centers. In this context, this study evaluates the potential for green hydrogen production in the State of Ceará, Brazil, using multi-criteria decision-making (MCDM) methods, which are designed to prioritize conflicting tangible and intangible criteria to determine the best decision alternatives. The additive ratio assessment (ARAS), simple additive weighting (SAW), combinative distance-based assessment (CODAS), and technique for order performance by similarity to ideal solution (TOPSIS) methods are evaluated for decision-making purposes, with weighting and aggregation relying on the best-worst method (BWM) and ensemble ranking. Criteria considered in the analysis include suitable areas for wind and solar farms, wind and solar energy potentials, water availability, surface temperature, electrical infrastructure, population, and local gross domestic product (GDP). The municipalities with the highest potential for renewable hydrogen production identified are Tauá (638 kton/year), Araripe (471 kton/year), and Beberibe (462 kton/year). According to the results, Araripe emerges as the most suitable municipality for green hydrogen production in Ceará.

Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients

Wind farm layout optimization (WFLO) studies often aim to maximize the annual energy production (AEP) of a wind farm by choosing an arrangement of turbines that minimizes wake interactions. One way to reduce the cost of WFLO studies is by using more computationally efficient AEP models. The cost of standard AEP modeling approaches, based on the numerical integration of low-fidelity engineering wake models, scales poorly with the number of simulated discrete wind conditions. A second way to reduce cost when using a gradient-based algorithm is to supply exact gradient information instead of finite-difference estimates. However, analytical functions for the derivatives of AEP with respect to turbine positions are not always available in the conventional modeling approach. FLOWERS is a computationally inexpensive, analytical model for wind farm AEP that is specifically developed for WFLO applications. In this paper, we analyze the performance of the FLOWERS AEP model with analytic gradients in a layout optimization study compared with a reference optimization framework across three wind farm case studies. We find that the FLOWERS-based approach reduces computation time by a factor of 50–4000 and improves optimal AEP by about 0.3% with less than half of the variability in AEP across instances with randomized initial conditions. We also find the optimal layouts to be insensitive to model parameter tuning, making FLOWERS-based layout optimization a streamlined, user-friendly approach.

Towards machine learning applications for structural load and power assessment of wind turbine: An engineering perspective

Over the past decades, the increasing energy demand has accelerated the construction of wind farms, raising higher expectations for precise load and power assessments in wind turbine performance. Traditional methods, which rely on analytical wake models and performance curves, often fail to adapt to complex inflow scenarios, leading to significant inaccuracies in predicting turbine loads and power output. This research addresses these challenges by introducing a novel two-phase framework for various phases of wind farm planning and development, using the NREL 5MW baseline wind turbine as a case study. The first part involves deriving recommended values for simplified thrust modulation factors at the preliminary design phase, enabling swift evaluation of maximum and fatigue thrust loads crucial for wind farm optimization. The second part focuses on designing and training a machine learning model at the detailed design phase. A gradient-boosting-based framework based on LightGBM provides comprehensive methods for assessing wind turbine load and power, enhancing the precision and efficiency of these assessments. The proposed model achieves significant improvements in predictive accuracy, achieving mean R-Squared of 0.995, 0.988, and 0.995 for power, peak load, and damage equivalent load evaluation, respectively. The framework streamlines the assessment process, enhancing both the accuracy and speed of power and load evaluations for wind farm design. This is expected to reduce computational costs and improve the effectiveness of downstream tasks, such as layout optimization and wake steering.

Probabilistic Analysis of Power System Frequency Stability Considering Wake Effects of Wind Farms

Probabilistic Analysis of Power System Frequency Stability Considering Wake Effects of Wind Farms

Optimizing Wind farm layout using a one-by-one replacement mechanism-incorporated gradient-based optimizer

Optimizing Wind farm layout using a one-by-one replacement mechanism-incorporated gradient-based optimizer

Optimized Two-Stage Planning Model for Integrating Compressed Air Energy Storage With Uncertain Correlated Wind Farms in Power Systems

Optimized Two-Stage Planning Model for Integrating Compressed Air Energy Storage With Uncertain Correlated Wind Farms in Power Systems

Control strategy of DC breaking resistor in offshore wind farm DC integrating system

Control strategy of DC breaking resistor in offshore wind farm DC integrating system

Research on capacity configuration scheme of hybrid energy storage equipment in wind farm based on wavelet packet decomposition

Research on capacity configuration scheme of hybrid energy storage equipment in wind farm based on wavelet packet decomposition

Global potential for seaweed aquaculture on existing offshore infrastructure.

Seaweed aquaculture is growing 8.9% annually to a forecast US$ 22.13 billion in 2024 and has several environmental, economic and social co-benefits. A proposed approach to expanding seaweed aquaculture is to develop offshore aquaculture. However, the costs of the infrastructure remain a key barrier. Reducing costs by growing seaweed on obsolete oil and gas infrastructure and offshore wind farms may make offshore seaweed cultivation economically viable, but the scale of opportunity needs to be clarified. We estimate an area of 53,387-106,774ha is currently available to cultivate seaweed on offshore wind farms, which could yield 736,831-4,441,116 tonnes of dry seaweed annually. Existing offshore oil and gas infrastructure could create 1,357,530-5,430,120ha for growing seaweed. Using 2019 seaweed prices, seaweed production on offshore wind farms and oil and gas infrastructure could return USD $3356 - $50,320 Million, sequester 687,784-8,616,449 tonnes of CO₂, and subsequent low carbon seaweed products could displace 987,642-33,221,364 tonnes of CO₂ emissions. While the scale of opportunity is significant, we caution that the regulatory and engineering costs of 'keeping the lights on' for decommissioned oil and gas infrastructure may make costs prohibitively high for seaweed cultivation. Even with existing infrastructure, the cost of cultivating seaweed offshore remains the largest barrier to implementation. A significant increase in market value for seaweed products is required to make offshore infrastructure economically viable. Similarly, many ecological and regulatory risks to offshore seaweed cultivation need to be further understood and minimised.