Economic and Reliability Value of Improved Offshore Wind Forecasting in Bulk Power Grid Operation: A Case Study of The New York Power Grid

TL;DR

This paper demonstrates that improved offshore wind forecasting, integrated with advanced reserve and reliability frameworks, significantly reduces costs and enhances reliability in the New York power grid.

Contribution

It introduces a machine-learning-based offshore wind forecast model and a risk-based reserve aggregation method tailored for grid operation, demonstrating economic and reliability benefits.

Findings

Forecast improvements reduce reserve procurement costs by 5.53%.

Risk-based reserve aggregation cuts total costs by 7.21%.

Enhanced forecasts decrease Loss of Load Probability by 19%.

Abstract

This study investigates the economic and reliability benefits of improved offshore wind forecasting for grid operations along the U.S. East Coast. We introduce and evaluate a state-of-the-art, machine-learning-based offshore wind forecasting model tailored for this region by integrating its improved forecasts into a dynamic reserve procurement framework aligned with New York Independent System Operator (NYISO) practices to evaluate their economic value. To determine system-wide reserve needs, plant-specific reserves are aggregated. However, conventional methods overlook spatial correlation across sites, often leading to over procurement. To address this, we propose a risk-based reserve aggregation technique that leverages spatial diversification. Additionally, we evaluate the reliability improvements enabled by the enhanced offshore wind forecast. To evaluate the operational impact, we propose an operational resource adequacy framework that captures uncertainty from forecast errors and grid conditions. Using this framework, we quantify key reliability metrics under different offshore wind forecast scenarios. Using New York State as a case study, we find that the improved forecast enables more accurate reserve estimation, reducing procurement costs by 5.53% in 2035 scenario compared to a well-validated numerical weather prediction model. Applying the risk-based aggregation further reduces total production costs by 7.21%. From a reliability perspective, the improved forecasts lower the system Loss of Load Probability (LOLP) by approximately 19% in the 2035 scenario, highlighting its potential to enhance system reliability during real-time grid operations.