Active and Reactive Power Scheduling Optimization using Firefly Algorithm to Improve Voltage Stability under Load Demand Variation

Abstract

This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.<span style="font-size: 9pt; font-family: Arial, sans-serif;" lang="EN-US">This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.</span>

Effect of Optimal Multi-DG Siting and Sizing in Transmission System using Hybrid Optimization Technique for Loss Control

“…Various studies were conducted to determine the optimal location and/or size of DG in maximizing the overall power system efficiency at the distribution side [5][6][7][8][9][10].Nonetheless, only a few studies were conducted to investigate effects of DG installation to the transmission network. These limited works concluded that medium sized DGs are able to reduce transmission network loss and improve voltage stability when DG location, DG size or both, were optimally selected [11][12][13].…”

Effect of optimal Multi-DG siting and sizing in transmission system using hybrid optimization technique for voltage control

“… Effect of optimal multi-DG siting and sizing in transmission system… (Sharifah A. Shaaya) 647 ability to deliver or absorb reactive power [13,14]. These studies found that different type of DG will affect power network performance differently.Inventing robust and reliable optimization technique to determine optimal DG location and size is currently trending in the researchers community, aiming at alleviating setbacks in the existing optimization techniques [15][16][17][18][19][20]. Apart from finding higher probability of optimum solution towards meeting the objective functions, new optimization algorithms are developed to reduce computational burden in the classical optimization techniques [21,22] Brainstorm Optimization (BSO) is a newly developed algorithm that made its appearance in 2011.…”