Optimal Network Reconfiguration (NR) is a well-accepted approach to minimize power loss and enhance voltage profile in the Electrical Distribution Networks (EDN). Since the NR problem contains huge combinational search space, most researchers consider the meta-heuristic techniques to attain NR solution. However, these meta-heuristic techniques do not guarantee to obtain the optimal solution besides they require large processing time to converge. This is mainly due to (1) random initialization and updating of population and (2) the continuous verification of population during the search process. With the aim of reducing the computational time and improving the consistency in obtaining the optimal solution as well as minimizing power loss and enhancing the voltage profile of the EDN, this work proposes a new method based on two-stage optimizations. The proposed method introduces an approach to simplify the network into simplified network graph. Then, this approach is utilized for guided initializations and generations of the population and for the proper population’s codification. The proposed method is implemented using the firefly algorithm and verified on 33-bus and 118-bus test systems. The results show the ability of the proposed method to obtain the optimal solution within fast computational time and with superior consistency compared to the conventional methods.

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.

Renewable Energy Resources (RERs) are a promising source of energy with hardly any pollution. Due to the intermittent nature from the output power of the nondispatchable RER and the load variations in the distribution network, it is important to frequently perform Network Reconfiguration (NR) to minimize the power loss and improve the network's voltage profile. Finding the optimal NR while simultaneously considering the dispatchable RER integration is important but challenging because of the complex combinational nature of the problem, and therefore, it is commonly solved by meta-heuristic techniques. However, the conventional meta-heuristic techniques involve random initializations and normally generate many nonfeasible solutions, which obstruct the search process. With the aim of improving the accuracy and consistency of the solution, this study proposes a two-stage method using biogeography-based optimization to attain the NR simultaneously with the RER placement and sizing for the sake of minimizing power loss and voltage deviation. The first stage simplifies the distribution network and then utilizes the simplified form to provide a better set of initial solutions and to maintain the network radiality. Thereafter, the second stage finds the final NR and RER locations and sizes. The simulations are carried out on 33-bus, 69-bus, and 118-bus systems, and the results are compared with previously published methods and some well-known optimization methods. In addition, load variations and RER's uncertainty are also considered. The obtained results show that the proposed method outperforms the existing methods. The results also indicate the significance of the hourly NR in reducing power loss.