Composite power system reliability assessment using maximum capacity flow and directed Binary Particle Swarm Optimization

Abstract

This work introduces a new technique to reduce the search space in evaluating reliability indices of power systems. A new formulation for maximum capacity flow of the transmission lines was developed and used in truncating the state space to identify the definite success and definite failure subspaces. A new algorithm based on "directed" Binary Particle Swarm Optimization (BPSO) was developed to search for the designated success or failure states in the remaining part of the state space. The remaining subspace is system dependent, and in case of systems with high capacity and reliability transmission lines in comparison with generation and loading levels, the effect of this subspace on system reliability can be ignored without causing any significant error. This method is very simple and straight forward, yet it reduces the computational time significantly. The method progresses by calculating the total available power carrying capability at every bus and between buses for every system state. Incidence matrix technique was used in building Line Capacity Matrix (LCM). From the LCM and system state (GenerationTransmission-Load), reliability indices can be calculated. This method was applied on two systems, the IEEE RTS and the modified IEEE RTS and it gave promising results.

A risk sensitivity-based approach to hardening power systems against catastrophic failures

“…To investigate the benefits of the heuristic technique and the BPSO, we have performed the same case study using Monte Carlo simulation. The computation time using the heuristic technique with the BPS was around 45% less than the computation time using Monte Carlo simulation which confirms with the results of [15].…”

“…Any state in the state space that has a generation vector larger than or equal to the minimum generation vector is guaranteed to be a success state. The failure subspace can be determined using a technique that has been proposed in [15]. By examining the above non-linear optimization problem, it can be noted that for any sampled state, if one of the following two conditions is satisfied, we can conclude that this state is a failure state:…”

“…It is well known that the transmission lines of the IEEE RTS are very reliable and they have high power carrying capabilities in comparison with the loading level of the system. The contributions of the failures of these transmission lines and their capacity limits on the annual and annualized indices are very small and can be ignored [5], [15], [16]. For instance, the annualized LOLP index of the IEEE RTS using DC load flow model is 0.08344.…”

“…A comparative study of using (PIS) methods specifically GA, repulsive binary particle swarm optimization (RBPSO) and binary ant colony optimization (BACO) was introduced in [11]. Further, a search space reduction using PSO was introduced in [15], [16].…”

Reliability and sensitivity analysis of composite power systems considering voltage and reactive power constraints

“…A comparative study of using PIS methods specifically GAs, repulsive binary PSO and binary ant colony optimisation has been introduced in [16]. A search space reduction using the PSO technique was introduced in [19,20]. This paper investigates the effects of the voltage and reactive power constraints on composite system reliability indices through performing sensitivity analyses of the severity index with respect to the voltage and reactive power constraints.…”