1 www.futminna.edu.ng www.seetconf.futminna.edu.ng APPLICATION OF ANALYTICAL-FIREFLY ALGORITHM FOR OPTIMAL LOCATION AND SIZING OF DISTRIBUTED GENERATOR IN STANDARD IEEE 30-BUS DISTRIBUTION NETWORK Abdulrahman Olaniyan 1 , Jimoh Boyi 2 , Yusuf Jibril 3 1,2,3 Ahmadu Bello University, Zaria * olaniyanabdulrahman@gmail.com, +2348027331685. ABSTRACT This paper presents a combined method which integrates the analytical approach into a firefly algorithm for optimal location and sizing of Distributed Generators (DG) in power system distribution networks. Optimal location and sizing of DGs are key to achieving an improvement in the system’s reliability, stability and reduction of power losses. The combined method was then tested on the standard IEEE 30-bus radial distribution system and results obtained showed high precision for location and accuracy in sizing. Keywords: Distributed Generation, Analytical Approach, Firefly Algorithm, Loss Minimization 1. INTRODUCTION Due to continuous economic development which has brought about an increase in load demands in distribution networks, developing nations like Nigeria, have their power systems operating very close to the voltage instability boundaries. The decline in voltage stability margin is one of the important factors which restricts increment in load served by distribution companies (Jain et al., 2014). This rapidly increasing need to improve the stability and reliability of power systems, the need to provide more electrical power and difficulties in providing the required capacity using traditional solutions, such as transmission network expansions and substation upgrades, provide a motivation to select a Distributed Generation (DG) option. A DG is understood to be a type of generation system which is embedded, integrated and controlled on the distribution voltage side of the power system network. DG can be integrated into distribution systems to improve voltage profiles, power quality and the system generally. (Muttaqi et al., 2014). These DG units when integrated into distribution networks provide ancillary services such as spinning reserve, reactive power support, loss compensation, and frequency control. On the other hand, poorly planned and improperly operated DG units can lead to reverse power flows, excessive power losses and subsequent feeder overloads (Atwa et al., 2010). Several methods, objectives and constraints for finding the optimal location and size of DGs in distribution networks have been introduced by different researchers. Methods used include the classical or numerical method by Atwa et al., (2010), Ochoa & Harrison, (2011) and Rau & Wan, (1994); the analytical approach by Wang & Nehrir, (2004), Acharya et al., (2006), Gözel & Hocaoglu, (2009) and Hung et al., (2010), Hung et al., (2013), Hung et al., (2014). Another method used is the heuristic approach by Abou El- Ela et al., (2010), Akorede et al., (2011), López-Lezama et al., (2012), Soroudi & Ehsan, (2011) and Vinothkumar & Selvan, (2011, 2012). Combined and hybrid solution approaches were also proposed in Afzalan & Taghikhani, (2012) and Moradi & Abedini, (2012). These methods have also presented different kinds of objective functions varying from single to multiple objectives and different types of constraints have also been addressed. The analytical method for optimal DG allocation was developed based on the 2/3 rule for capacitor placement. It is simple to understand and non-iterative but it can only get an approximate solution. Furthermore, the analytical method is computationally exhaustive and time consuming thus it becomes more