Vattenfall (Germany)

ABSTRACT This study evaluates the expected long‐term performance and environmental effects of galvanic anode cathodic protection (GACP) systems for offshore wind monopiles. A numerical model was developed and compared to field data to simulate protection performance over a 25‐year design life. The model incorporates key factors such as coating degradation, oxygen limiting current density, surface film resistance, dimensional changes of the anode over time, and anode quantity. Based on the simulations, the metal emission of Al‐Zn‐In anodes per monopile was estimated at approximately 20%–36% of the total anode mass, corresponding to configurations with 50 and 25 anodes, respectively. This results in total metal emissions of 0.091–0.111 ppm Al and 0.005–0.006 ppm Zn. While significantly exceeding natural background levels, the predicted concentrations remain 11–13 times below reported toxicity limits for aluminum and 6–8 times below those for zinc. The results highlight the importance of integrating field data and long‐term monitoring to validate environmental impact assessments and ensure CP systems are both effective and sustainable.

<strong class="journal-contentHeaderColor">Abstract.</strong> Blade leading-edge erosion (LEE), atmospheric turbulence intensity (<em>TI</em>) and shear can significantly impact wind turbine performance and annual energy production (AEP). This study employs aeroelastic simulations to investigate their combined effects. An offshore original equipment manufacturer (OEM) provided aeroelastic model was used to simulate various scenarios. Turbulence intensity was varied for a range of wind speeds and the blade polars were modified to simulate different degrees of erosion, represented by varying levels of roughness. Also, simulations with and without the inclusion of wind shear were investigated. Findings reveal that even mild simulated erosion can reduce AEP by 0.82 %, while more severe erosion leads to a 2.83 % decrease. Increasing <em>TI</em> exacerbates these losses, with a 25 % <em>TI</em> causing up to a 3.5 % AEP reduction for eroded blades. These effects were most pronounced at lower wind speeds. Furthermore, standard time-averaging practices in power curve analyses can obscure the true magnitude of <em>TI</em> and LEE's impact on short-term power fluctuations. This work emphasises the critical importance of considering both blade condition and <em>TI</em> for accurate AEP assessments, optimal maintenance scheduling and improved wind turbine design in the context of site-specific atmospheric conditions.

Performances of multi-fidelity numerical models in reproducing the impacts of internal and external wakes on power production are evaluated against SCADA data from two operational offshore wind farms in the North Sea. Results obtained for two engineering wake models, Jensen and Cumulative Curl (default and tuned settings), and a higher-fidelity Reynolds-averaged Navier-Stokes (RANS) setup are presented here. Tuned engineering models were configured against RANS results for an idealized wind farm layout consisting of several turbine setups. Input data for the model evaluation scenarios was obtained from a met mast located between the wind farms and binned according to atmospheric stability, wind speed and direction, and turbulence intensity. Results show that the higher fidelity of RANS is most advantageous for inflows that generate more complex internal wakes (e.g., deep array effects) and in replicating external wake impacts. The tuned setups of the engineering models, modified according to stability, outperform their default setups. Default model properties are defined in accordance with developer recommendations. One of the tuned models shows close farm-level agreements with RANS and even exceeds its performances in some of the cases of lower flow complexity.

This study investigates how blade aerodynamic modifications, including Leading Edge Roughness (LER), influence offshore wind turbine performance over their operational lifespan. Developing a novel methodology, this research analyses data from twelve multi-megawatt turbines over a twelve-year period, focusing on the intricate relationship between blade erosion, blade enhancements, operations and maintenance events, control PLC parameter updates, and their cumulative impact on turbine efficiency. The analysis hinges on the integration of SCADA data, Operations and Maintenance (O&M) records, and air density corrections. A key contribution is the development of a Turbine Performance Integral (TPI) method, which leverages generator speed and power output data to track performance trajectories. Seasonal-Trend decomposition using Locally Estimated Scatterplot Smoothing (STL) further isolates long-term trends and seasonal variations in performance. Overcoming data availability and quality limitations, the study reveals significant findings concerning software updates impacts on turbine control strategies, the variable effects of blade repairs and enhancements and the complex interaction between O&M events and performance. This study's strength lies in its methodical approach and statistical rigour, offering a path forward in the quest for optimised wind turbine efficiency and advancing renewable energy. The detrimental effect of Leading Edge Roughness (LER) or Leading Edge Erosion (LEE) on aerofoil characteristics has been investigated through wind tunnel experiments and various studies on the impact of erosion and roughness on wind turbine annual energy production (AEP) Mishnaevsky Jr et al. (2021). Predictions suggest that erosion related annual energy losses of up to 7% may occur described in various publications Han et al. (2018) Maniaci et al. (2016) Bak et al. (2020) Bak (2022). However, a key challenge remains in identifying and validating these computed energy losses when analysing SCADA data from operational wind turbines Ding et al. (2022) . This challenge is manifested by a continued absence of an established correlation between blade erosion and turbine performance underscoring a lack of deeper understanding of the underlying reasons.

Abstract. This study investigates how blade aerodynamic modifications, including Leading Edge Roughness (LER), influence offshore wind turbine performance over their operational lifespan. Developing a novel methodology, this research analyses data from twelve multi-megawatt turbines over a twelve-year period, focusing on the intricate relationship between blade erosion, blade enhancements, operations and maintenance events, control PLC parameter updates, and their cumulative impact on turbine efficiency. The analysis hinges on the integration of SCADA data, Operations and Maintenance (O\\&amp;amp;M) records, and air density corrections. A key contribution is the development of a Turbine Performance Integral (TPI) method, which leverages generator speed and power output data to track performance trajectories. Seasonal-Trend decomposition using Locally Estimated Scatterplot Smoothing (STL) further isolates long-term trends and seasonal variations in performance. Overcoming data availability and quality limitations, the study reveals significant findings concerning software updates impacts on turbine control strategies, the variable effects of blade repairs and enhancements and the complex interaction between O\\&amp;amp;M events and performance. This study's strength lies in its methodical approach and statistical rigour, offering a path forward in the quest for optimised wind turbine efficiency and advancing renewable energy.

The progressing introduction of renewable energy sources (RES) in the energy system creates an increasingly difficult market environment for traditional utility companies. While their fossil-fueled power plants are important to secure the supply of electricity, the associated operations are costly and energy intensive. This paper presents a simulation study on an open-pit mine (OPM), where flexibilities are sought for increasing the operator's overall energy efficiency. For this purpose, the OPM's material flows and energy flows are simulated simultaneously. Drawing from discrete production systems in a "cross-learning" approach possibilities for and adjustments to the process control are investigated. Preliminary results show that some opportunities to increase efficiency exist but still require more scrutiny.

For determination of battery storage business cases key decision criteria are lifetime and State of Health (SoH), in addition to investment costs, operating costs and revenues. The lifetime and SoH strongly depend on operating conditions, such as number of equivalent full cycles (EFC), operating battery cell temperature, Depth of Discharge (DoD), C-rate and State of Charge (SoC). Those can vary depending on operating strategy and chosen application. In this analysis the lifetime of 2nd-life lithium-ion (Li-ion) battery energy storage systems (BESS) are examined and evaluated, depending on various stationary applications. Avoiding the expenses for battery cell parameterization, an improved methodology based on weighted Ah-Throughput (AhTp) and Fuzzy Logic is used to apply qualitative statements of experts into quantitative values. The investigation is carried out by using MATLAB Simulink including application-related load profiles. Based on the results statements for lifetime expectations are given.

In order to meet future demands, existing and new plants need to be optimized to offer additional control reserves to stabilize an electrical grid, which is highly penetrated by fluctuating renewables. Since this requires a dynamic investigation, transient physical based models of different power plants have been developed to evaluate effects of increased flexibility as well as to develop optimization strategies. The approach has been tested for a specific 500 MW lignite-fired power plant. It includes detailed modeling of the incorporated sub-systems and their interactions as well as the implementation of the power plant’s control system. The dynamic simulation model is used for the identification of energy storage potentials within the process and for testing and developing control strategies in order to increase flexibility and marketable output of the process. The strategies are benchmarked and evaluated based on the consideration of exergetic efficiency and lifetime-consumption of critical components.