A machine learning-based approach for maximizing system profit in a power system by imbalance price curtailment

Abstract

The integration of wind farms into the power grid is difficult due to unpredictable wind speed fluctuations. This variation has an impact on power generation profitability, demanding effective forecasting to lessen pricing risks. A novel optimization approach is proposed in this paper to expand social welfare and profitability while increasing revenue for power generators. This method is crucial for avoiding financial risks related to variable wind patterns. Narrowing the gap between anticipated and actual wind speeds (WS_AN, WS_AC) can lessen the negative impact of imbalanced prices on profitability. This reduction is necessary to enhance the economic performance of the power system. The paper endorses the use of machine learning (ML) techniques, specifically Long Short-Term Memory (LSTM) and Random Forest (RF) methods, to precisely anticipate wind speed. These models serve as analytical tools for enlightening decision-making and resource allocation in wind energy generation. According to the study, pricing imbalances have a major impression on profit calculations in deregulated systems. The empirical data show that effective forecasting can expand financial outcomes for energy companies, reducing risks and maximizing revenue. Finally, the empirical results highlight the significance of accurate wind speed forecasts and the use of advanced optimization approaches in growing the profitability and efficiency of renewable energy-dependent power systems. These findings offer a strong foundation for further research and use of machine learning techniques in the energy sector. The optimization model was accomplished with modified IEEE 14 bus test systems in this work.