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The integration of wind turbines and wind farms into the power grid presents significant challenges, including the variable and uncertain nature of wind power, the need for efficient transmission and distribution of power, and the need for advanced control and monitoring systems. Recent advances in wind turbine and wind farm technology have greatly improved their ability to integrate into the power grid. This includes the use of variable speed generators and power electronics, such as inverters, which allow for more accurate control of the power output and better alignment with the grid's frequency. Additionally, advanced control algorithms, such as model predictive control and feedback linearization, have been developed to improve the stability and reliability of wind power systems. Communication systems have also been integrated into wind power systems to allow for real-time monitoring and control of the wind farm's performance. This improves the ability of the wind farm to provide ancillary services, such as frequency regulation and voltage control. In this work, the major interconnection requirements for the U.S. grid for the expected mass integration of wind farms were discussed focusing on sustainable and reliable control strategies. The paper explores various control schemes that are used for providing response for grid reliability and sustainability. The analysis also included the future steps that OEMs of wind turbines need to take and the regulatory framework that is needed for ancillary services to provide an equitable platform for all generating units – conventional and renewables. [ABSTRACT FROM AUTHOR]

The South Korean government is encouraging the active participation of power generation companies in the offshore wind power project by announcing the renewable energy certificates (REC) weighting plan. However, from a long-term perspective, the offshore wind power must be able to generate profits without government support to demonstrate its business feasibility and attract the voluntary participation of power generation companies. This is because government support may be subject to change, depending on the internal and external political circumstances of the country. This report calculates the expected costs for a 495 MW floating offshore wind farm in South Korea's market environment and examines how the feasibility of the project shifts depending on the country's current REC weights. Furthermore, this study intends to determine whether floating offshore wind power can generate profits without the Korean government's support by calculating the expected profit in combination with the green hydrogen project. The net present value, levelized cost of energy and internal rate of return (IRR) indexes are calculated according to the project's specific particularities, such as power purchase agreement, REC weighting, distance from shore and sea depth. Based on this, an index-based comparison is revealed and the margin for profitability for such an investment is discussed. The calculation results revealed that with a decrease in capital expenditures and operating expenditure or an increase in the system marginal price under specific assumptions, the value of IRR increased. [ABSTRACT FROM AUTHOR]

Most forecasting algorithms are tuned to a specific location or dataset and will not perform well in other situations. Some wind speed data might contain outliers, missed values, or noise which affects the forecasting performance tremendously. This paper proposes a hybrid-forecasting model that includes pre-processing and deep learning techniques to bridge the gap in developing a generic forecasting algorithm that is unspecific of location or dataset. The proposed model includes a preprocessing part that consists of missed value and outlier handling, decomposition, Kalman filtering, and smoothing. This is an important step because no matter how accurate the forecasting model is, results will vary tremendously if the dataset is corrupted. In addition to that, three different deep-base learning algorithms RNN, GRU, and LSTM will be used based on the characteristics of each subseries to reduce the complexity of the overall forecasting model. The proposed model performed the best across the seven tested sites from different locations with different climates and geography. Compared to other forecasting models such as LSTM standalone and EWT-LSTM, a performance improvement in accuracy by 50% as well as a 25% reduction in processing time was achieved with the proposed forecasting model [ABSTRACT FROM AUTHOR]