Optimal flexible alternative current transmission system device allocation under system fluctuations due to demand and renewable generation

This study proposes two methods for the optimal placement of flexible alternative current transmission system (FACTS) devices considering variations in demand and renewable generation output. The basic optimisation technique utilised is the differential evolution algorithm and the objective is to minimise the cost of generation. The static performance of the FACTS device is considered here. Simulation shows that with renewable generation present in the network, the system state at peak demand is not always the most suitable state to use for the determination of the optimal FACTS allocation. From this, techniques based on the Monte Carlo simulation are proposed to determine the location for which the operation of FACTS device gives highest benefit in terms of saving cost of conventional generation. These techniques collectively are called renewable uncertainty-based optimal FACTS allocation techniques. This study shows the effectiveness of the techniques in the determination of the optimal FACTS placement for networks with a high penetration of renewable generation

Employment of power transfer distribution factor for the optimal placement of FACTS devices

This paper presents a method for determination of the optimal placement and sizing of FACTS device. The objective considered is to maximize the available transfer capability (ATC) of heavy transaction zones. Generally, ATC calculation is recursive and consequently time consuming. In this paper the power transfer distribution factor (PTDF) is used for fast calculation of the ATC. The method is modified to include losses and subsequently, the FACTS placement problem is solved using a genetic algorithm to obtain the least loss at the maximum ATC. For the purpose of comparison the ATC is also calculated using the repeated power flows method. This paper shows that the PTDF method is both computational more efficient and provides a sufficient degree of solution accuracy