Computationally Efficient Day-Ahead OPF using Post-Optimal Analysis with Renewable and Load Uncertainties

TL;DR

This paper introduces a computationally efficient method for day-ahead optimal power flow that manages renewable and load uncertainties without needing their probability distributions, using post-optimal analysis and a new participation factor.

Contribution

It develops a novel participation factor based on current optimal basis, enabling uncertainty handling through sensitivity analysis without requiring uncertainty distribution information.

Findings

Effective handling of renewable and load uncertainties demonstrated on IEEE 30-Bus system.

Method ensures optimality and constraint satisfaction under uncertainties.

Applicable to single and multiple bus uncertainties in power systems.

Abstract

This paper presents a method to handle renewable source and load uncertainties in Dynamic Day-ahead Optimal Power Flow (DA-OPF) using post-optimal analysis of linear programming problem. The method does not require the uncertainty distribution information to handle it. A new Participation Factor (PF) to distribute changes caused by uncertainty has been developed based on the current optimal basis. The proposed PF takes care of all the constraints and ensures optimality with uncertain renewable generation and load using Sensitivity Analysis (SA) and Individual Tolerance Ranges (ITR) for individual and multiple simultaneous changes respectively. For quantification of confidence level, standard density distribution of solar power output and load is used. The test results on IEEE 30-Bus establishes the applicability of the proposed method for handling single and multiple bus uncertainties.