Weather-Driven Flexibility Reserve Procurement: A NYISO Offshore Wind Power Case Study
Zhirui Liang, Robert Mieth, Yury Dvorkin, Miguel A. Ortega-Vazquez

TL;DR
This paper introduces a weather-driven probabilistic wind power forecasting method and a risk-based reserve sizing model, improving flexibility reserve procurement for offshore wind in NYISO systems by reducing costs and enhancing security.
Contribution
It presents a novel weather-based probabilistic forecasting approach and a risk-aware reserve sizing model suitable for new wind farms lacking historical data.
Findings
Weather-driven forecasts improve reserve accuracy
Risk-based procurement reduces system costs
Enhanced security compared to industry practice
Abstract
The growing penetration of variable renewable energy sources (VRES) requires additional flexibility reserve to ensure reliable power system operations. Current industry practice typically assumes a certain fraction of the VRES power production forecast as flexibility reserve, thus ignoring other relevant information, such as weather conditions. To address this, probability- and risk-based reserve sizing models have been proposed, which use probabilistic VRES power forecasts that mostly rely on historical forecast and actual VRES power data for model training. Hence, these approaches are not suitable for planned or newly installed wind farms, where no or insufficient historical data is available. This paper addresses this caveat. First, we propose a weather-driven probabilistic forecasting method for wind power installations using publicly available weather data. Second, we apply the…
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Taxonomy
TopicsEnergy Load and Power Forecasting · Electric Power System Optimization · Integrated Energy Systems Optimization
