Reliability Assessment of Power System Based on the Dichotomy Method

TL;DR

This paper introduces a novel dichotomy method for power system reliability assessment that significantly improves efficiency over traditional methods by partitioning the state space and employing targeted Monte Carlo sampling.

Contribution

The paper proposes a dichotomy-based lattice partitioning approach combined with customized Monte Carlo sampling for faster and more accurate power system reliability evaluation.

Findings

Achieves the analytic LOLP with hundreds of OPF operations on RBTS system.

Converges rapidly with thousands of OPF operations on test systems.

Outperforms traditional methods by hundreds of times in efficiency.

Abstract

With a sustainable increase in the scale of power system, the number of states in the state space grows exponentially, and the reliability assessment of the power system faces enormous challenges. Traditional state-by-state assessment methods, such as state enumeration (SE) and Monte Carlo simulation (MCS) methods, have encountered performance bottlenecks in terms of efficiency and accuracy. In this paper, the Boolean lattice representation theory of the state space was studied, and a dichotomy method was proposed to efficiently partition the state space into some disjoint sub-lattices with a relatively small number of optimal power flow (OPF) operations. Based on lattice partition, the reliability indices of the entire space can be calculated lattice-by-lattice. In addition, alone with the partitioning procedure, the calculated loss of load probability (LOLP) monotonically increases and rapidly tends to the analytic value with the designated error bound. Moreover, we designed a customized Monte Carlo sampling method in lattices of interest to compute expected energy not supply (EENS). The experiments are conducted on the RBTS and RTS-79 systems. The results show that the proposed method achieves the analytic LOLP of the RBTS system after five hundreds of OPF operations, which is about hundreds of times faster than traditional methods, and the designed Monte Carlo sampling method converged after thousands of OPF operations on test systems.