With growing interest in sustainability and net-zero emissions, there has been a global trend to integrate wind power into energy grids. However, challenges such as the intermittency of wind energy remain, which leads to a significant need for accurate wind-power forecasting. Therefore, this study focuses on creating a wind-power generation-forecasting model using a machine-learning algorithm. In this study, we used the gradient-boosting machine (GBM) algorithm to build a wind-power forecasting model. Time-series data with a 15 min interval from Jeju’s wind farms were applied to the model as input data. The short-term forecasting model trained by the same month with the test set turns out to have the best performance, with an NMAE value of 5.15%. Furthermore, the forecasting results were applied to Jeju’s power system to carry out a grid-security analysis. The improved accuracy of wind-power forecasting and its impact on the security of electrical grids in this study potentially contributes to greater integration of wind energy.

As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.

The renewable-plus-storage power plant is becoming economically viable for power producers given the maturing technology and continued cost reduction. However, as batteries and power conversion systems remain costly, the power plant profitability depends on the capacity determination of the battery energy storage system (BESS). This study explored an approach for optimal capacity determination of a BESS combined with renewable energy considering the complex degradation of lithium-ion batteries. The proposed sizing algorithm iteratively evaluates the effect of BESS operation on battery degradation and estimates the cash flows of the power plant. In addition, we studied battery augmentation that adds the storage capacity in the base system to sustain the BESS capacity throughout the project planning horizon. Using data from South Korea, we showed that both the optimal storage capacity and project profitability are higher when the BESS is combined with solar generation than when combined with wind generation. Moreover, simulation results demonstrated that the proposed battery augmentation scheme improves the project profitability by deferring the upfront cost of batteries and increasing the total revenue. The proposed approach can provide a comprehensive framework for the parties involved in a BESS project to accurately calculate the BESS sizes and maximize the project profitability.

The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integration. Ensemble forecasting, which combines several forecasting techniques, is considered a viable alternative to conventional single-model-based forecasting for improving the forecasting accuracy. In this work, we propose the day-ahead ensemble forecasting of wind power using statistical methods. The ensemble forecasting model consists of three single forecasting approaches: autoregressive integrated moving average with exogenous variable (ARIMAX), support vector regression (SVR), and the Monte Carlo simulation-based power curve model. To apply the methodology, we conducted forecasting using the historical data of wind farms located on Jeju Island, Korea. The results were compared between a single model and an ensemble model to demonstrate the validity of the proposed method.