Data-Driven Stochastic Optimization for Power Grids Scheduling under High Wind Penetration

TL;DR

This paper introduces a data-driven stochastic unit commitment method for power grid scheduling that effectively manages wind power uncertainty using historical data, scenario analysis, and parallel computing, enhancing efficiency and robustness.

Contribution

It develops a novel posterior predictive distribution for wind power uncertainty and a parallel optimization approach for reliable decision-making in power grid scheduling.

Findings

Outperforms existing deterministic and stochastic methods in efficiency.

Provides more robust scheduling under wind power uncertainty.

Demonstrated on six-bus and 118-bus systems.

Abstract

To address the environmental concern and improve the economic efficiency, the wind power is rapidly integrated into smart grids. However, the inherent uncertainty of wind energy raises operational challenges. To ensure the cost-efficient, reliable and robust operation, it is critically important to find the optimal decision that can correctly and rigorously hedge against all sources of uncertainty. In this paper, we propose data-driven stochastic unit commitment (SUC) to guide the power grids scheduling. Specifically, given the finite historical data, the posterior predictive distribution is developed to quantify the wind power prediction uncertainty accounting for both inherent stochastic uncertainty of wind power generation and input model estimation error. For complex power grid systems, a finite number of scenarios is used to estimate the expected cost in the planning horizon. To further control the impact of finite sampling error induced by using the sample average approximation (SAA), we propose a parallel computing based optimization solution methodology, which can quickly find the reliable optimal unit commitment decision hedging against various sources of uncertainty. The empirical study over six-bus and 118-bus systems demonstrates that our approach can provide more efficient and robust performance than the existing deterministic and stochastic unit commitment approaches.