This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON POWER SYSTEMS 1 Optimal Distributed Generation Allocation in Distribution Systems for Loss Minimization Karar Mahmoud, Student Member, IEEE, Naoto Yorino, Member, IEEE, and Abdella Ahmed Abstract—An efficient analytical (EA) method is proposed for optimally installing multiple distributed generation (DG) technolo- gies to minimize power loss in distribution systems. Different DG types are considered, and their power factors are optimally cal- culated. The proposed EA method is also applied to the problem of allocating an optimal mix of different DG types with various generation capabilities. Furthermore, the EA method is integrated with the optimal power flow (OPF) algorithm to develop a new method, EA-OPF which effectively addresses overall system con- straints. The proposed methods are tested using 33-bus and 69-bus distribution test systems. The calculated results are validated using the simulation results of the exact optimal solution obtained by an exhaustive OPF algorithm for both distribution test systems. The results show that the performances of the proposed methods are superior to existing methods in terms of computational speed and accuracy. Index Terms—DG power factor, distribution systems, optimal DG location, optimal DG size, power loss reduction. I. INTRODUCTION I N recent years, the use of distributed generation (DG) tech- nologies has remarkably increased worldwide due to their potential benefits. DG units generate power near load centers, avoiding the cost of transporting electric power through trans- mission lines. Another benefit of DG is cost savings in elec- tricity production compared with large centralized generation stations [1]. Furthermore, renewable DG technologies, such as wind power, photovoltaic (PV), and solar thermal systems, are considered to be one of the fundamental strategies in the fight against climate change, as they can reduce dependence on fossil fuels [2]–[5]. With the rapid increase of DG penetration, distribution systems are being converted from passive to active networks. Normally, DG units are small in size and modular in structure. Therefore, their impacts on distribution system operation, control, and stability vary depending on their locations and sizes [6], [7]. One of the most common positive impacts of DG is the ability to reduce distribution system losses [8]. However, Manuscript received June 19, 2014; revised October 29, 2014, November 26, 2014, January 20, 2015, March 09, 2015; accepted March 26, 2015. Paper no. TPWRS-00835-2014. K. Mahmoud is with the Faculty of Engineering, Aswan University, 81542 Aswan, Egypt, and also with the Graduate School of Engineering, Hiroshima University, 739-8527 Hiroshima, Japan (e-mail: karar.alnagar@aswu.edu.eg). N. Yorino is with the Graduate School of Engineering, Hiroshima University, 739-8527 Hiroshima, Japan (e-mail: yorino@hiroshima-u.ac.jp). A. Ahmed is with the Faculty of Engineering, Aswan University, 81542 Aswan, Egypt. Digital Object Identifier 10.1109/TPWRS.2015.2418333 inappropriate DG allocation may lead to increased system losses and system operation costs [9], [10]. It is also a fact that most of the electrical power losses in electric power systems are dissipated in distribution systems due to heavy currents flowing in primary and secondary feeders. Therefore, there is a critical need to develop efficient tools that can optimally allocate different DG types in distribution systems, thereby reducing losses. Several methods have recently been proposed for the plan- ning of distribution systems with DG to minimize losses. These methods can be classified as numerical-based (NB), heuristic- based (HB), and analytical-based (AB) methods [10]. The most common examples of NB methods are gradient search (GS) [11], linear programming (LP) [12], optimal power flow (OPF) [13], and exhaustive search (ES) [14], [15]. The GS, LP, and OPF algorithms are considered efficient ways for obtaining the optimal DG sizes at certain locations. The ES algorithm is based on searching for the optimal DG location for a given DG size or under different load models. Therefore, these methods fail to represent the accurate behavior of a DG optimization problem that involves two continuous variables, both optimal DG size and optimal DG location. The HB methods are based on em- ploying advanced artificial intelligence (AI) techniques, such as genetic algorithms (GAs) [16], [17], particle swarm optimiza- tion (PSO) [18], harmony search (HS) [19], and tabu search [20]. The main feature of these methods is their computational robustness. They can provide near-optimal solutions but involve intensive computational efforts. It is notable that great interest is directed to the AB methods, as they are easy to implement and fast. AB methods often follow various strategies to simplify the optimization problem, either by assuming uniformly distributed loads as in [21] or by allo- cating only a single DG unit in the entire system [21], [22]. Ref- erence [23] has proposed a method for determining the optimal locations of multiple DG units, while the corresponding optimal DG sizes are obtained by the Kalman filter algorithm. A load centroid concept [24] is proposed in [25] for allocating mul- tiple DG units. The authors of [26] have proposed an approach to allocate a single DG unit that operates at unity power factor, which has recently been extended to an improved analytical (IA) method [27]. The IA method involves allocating a single DG with various capabilities to generate both active and reactive power. More recently, the IA method has been upgraded to solve the multiple DG allocation problem [28] and validated by com- parison with the exhaustive power flow solution. The main idea of the IA method for allocating multiple DG units is to update the load data after each time the DG is allocated to determine the next DG location. After each DG placement, the calculated 0885-8950 © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.