Research on OPF control of three-phase four-wire low-voltage distribution network considering uncertainty

TL;DR

This paper introduces a robust stochastic optimization-based OPF control method for three-phase four-wire low-voltage distribution networks that effectively manages uncertainties, balances phases, and reduces operational costs without needing communication infrastructure.

Contribution

It presents a novel RSO-based OPF control approach utilizing deep learning for uncertainty management in low-voltage distribution networks.

Findings

Effective voltage and current control achieved

Operational costs minimized

Three-phase imbalance reduced within limits

Abstract

As power systems become more complex and uncertain, low-voltage distribution networks face numerous challenges, including three-phase imbalances caused by asymmetrical loads and distributed energy resources. We propose a robust stochastic optimization (RSO) based optimal power flow (OPF) control method for three-phase, four-wire low-voltage distribution networks that consider uncertainty to address these issues. Using historical data and deep learning classification methods, the proposed method simulates optimal system behaviour without requiring communication infrastructure. The simulation results verify that the proposed method effectively controls the voltage and current amplitude while minimizing the operational cost and three-phase imbalance within acceptable limits. The proposed method shows promise for managing uncertainties and optimizing performance in low-voltage distribution networks.