Evaluating Stochastic Methods in Power System Operations with Wind Power

TL;DR

This paper evaluates various stochastic operational strategies for power systems with wind power, focusing on probabilistic forecasting, scenario reduction, and optimization techniques to improve reliability and cost-efficiency.

Contribution

It introduces a comprehensive framework combining probabilistic wind forecasting, scenario reduction, and stochastic optimization for better power system operation with renewables.

Findings

Probabilistic wind forecasting improves scenario accuracy.

Stochastic optimization enhances reliability and reduces costs.

Scenario reduction decreases computational complexity.

Abstract

Wind power is playing an increasingly important role in electricity markets. However, it's inherent variability and uncertainty cause operational challenges and costs as more operating reserves are needed to maintain system reliability. Several operational strategies have been proposed to address these challenges, including advanced probabilistic wind forecasting techniques, dynamic operating reserves, and various unit commitment (UC) and economic dispatch (ED) strategies under uncertainty. This paper presents a consistent framework to evaluate different operational strategies in power system operations with renewable energy. We use conditional Kernel Density Estimation (KDE) for probabilistic wind power forecasting. Forecast scenarios are generated considering spatio-temporal correlations, and further reduced to lower the computational burden. Scenario-based stochastic programming with different decomposition techniques and interval optimization are tested to examine economic, reliability, and computational performance compared to deterministic UC/ED benchmarks. We present numerical results for a modified IEEE-118 bus system with realistic system load and wind data.