Optimizing wind power integration: Increasing profitability and market efficiency through machine learning based wind speed forecasting in Japan's Electricity Grid

Wind energy has become a key constituent in Japan's developing electricity sector. However, the fluctuation of wind speed in its deployment poses operational and economic risk, which puts the generators at risk for energy price imbalances (EP IM ). The forecast errors between predicted and actual wind output, which can result in imbalance penalties, negatively impact renewable producers’ profitability, obstruct deeper integration of renewables, and stagnate economic growth. Herein, a comprehensive optimization mechanism has been introduced in this work by integrating state-of-the-art machine-learning (ML) models, that is, Random Forest (RF) and Long Short-Term Memory (LSTM), with numerous metaheuristic optimization techniques, that is, Whale Optimization Algorithm, American Zebra Optimization (AZO) Algorithm, along with Sequential Quadratic Programing to maximize the system economy. The framework forecasts wind power and load demand concurrently through 24 h by embedding these into an optimal power flow framework: Maximizing profit for the system, and minimizing imbalance costs. It makes use of the real wind speed data of Tokyo for validation of the proposed method. The LSTM provides better forecasts than RF, and AZO continuously yields the best economic results after the implementation of the presented approach. The ML-metaheuristic approach facilitates dispatch efficiency, while simultaneously decreasing imbalance penalties and increasing the market profitability significantly. The proposed method is a technically feasible and operationally relevant approach to implementing a higher wind integration scale in Japan's deregulated market structure.