Real-Time Optimal Power Flow under Wind Energy Penetration-Part I: Approach

TL;DR

This paper proposes a prediction-updating method using parallel computing to enable real-time optimal power flow in power systems with high wind energy penetration, addressing the challenge of fast wind power fluctuations versus slow optimization response.

Contribution

It introduces a scenario-based prediction-updating approach with parallel computation to facilitate real-time optimal power flow under wind energy variability.

Findings

Uses scenario-based prediction to handle wind power uncertainty.

Employs parallel computing to solve multiple OPF problems simultaneously.

Creates a lookup-table for real-time reference operations.

Abstract

Real-time optimal power flow (RT-OPF) under wind energy penetration is highly desired but extremely difficult to realize. This is basically due to the conflict between the fast changes in wind power generation and the slow response from the optimization computation. This paper (Part I) presents a prediction-updating approach to address this challenge. We consider essential scenarios around forecasted data of wind power that would probably happen during the computation time required for solving a large-scale complex optimal power flow problem. Parallel computing is used to solve the individual OPF problems corresponding to these scenarios. This provides for the forecasted time horizon probable reference operations in the form of a lookup-table. One of these operations will be selected based on the actual wind power and realized to the grid for the current time interval, thus leading to a RT-OPF framework. The proposed approach is implemented in Part II of this paper using a 41-bus medium-voltage distribution network as a case study.