A team of researchers from Rutgers University, including Khaled Bin Walid, Feng Ye, Jiaxiang Ji, Ahmed Aziz Ezzat, Travis Miles, and Yazhou Leo Jiang, has conducted a study to assess the economic and reliability benefits of improved offshore wind forecasting for grid operations along the U.S. East Coast. Their work, published in the journal Applied Energy, focuses on integrating advanced forecasting models into grid operations to optimize reserve procurement and enhance system reliability.
The study introduces a state-of-the-art, machine-learning-based offshore wind forecasting model specifically tailored for the U.S. East Coast. This model is designed to provide more accurate predictions of wind energy generation, which is crucial for grid operators to manage the variability of wind power. The researchers integrated this improved forecast into a dynamic reserve procurement framework aligned with the practices of the New York Independent System Operator (NYISO). By doing so, they aimed to evaluate the economic value of these enhanced forecasts.
One of the key challenges addressed in the study is the conventional method of aggregating plant-specific reserves without considering spatial correlation across different wind sites. This often leads to over-procurement of reserves, which can be costly. To mitigate this issue, the researchers proposed a risk-based reserve aggregation technique that leverages spatial diversification. This approach takes into account the spatial correlation of wind conditions across different sites, leading to more efficient reserve procurement.
The study also evaluated the reliability improvements enabled by the enhanced offshore wind forecast. To assess the operational impact, the researchers developed an operational resource adequacy framework that captures uncertainty from forecast errors and grid conditions. This framework allowed them to quantify key reliability metrics under different offshore wind forecast scenarios. Using New York State as a case study, they found that the improved forecast enables more accurate reserve estimation, reducing procurement costs by 5.53% in the 2035 scenario compared to a well-validated numerical weather prediction model. Applying the risk-based aggregation further reduced total production costs by 7.21%.
From a reliability perspective, the improved forecasts lowered the system Loss of Load Probability (LOLP) by approximately 19% in the 2035 scenario. This highlights the potential of enhanced offshore wind forecasting to significantly improve system reliability during real-time grid operations. The study underscores the importance of integrating advanced forecasting models into grid operations to optimize reserve procurement and enhance the overall reliability of the power grid.
The research was published in the journal Applied Energy, providing valuable insights for grid operators and policymakers seeking to integrate more renewable energy sources into the power grid while maintaining system reliability and economic efficiency.
This article is based on research available at arXiv.