Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation - Khan, Abdullah (2022)
An optimal power flow (OPF) solution is an essential approach in electric power system operation. Electric service providers are continually working on power generation planning to improve different factors: electricity demands, deregulation of the markets, increasing utilization of distributed generation (DG), and the increasing role of decision-makers. These factors affect the operation plans, have raised the OPF problems' complexities, and require an unfailing optimization algorithm to solve economic and security concerns in different interconnected power systems. This thesis proposes and simulates the three novel optimization algorithms to handle DG allocation, different single-objective, and multi-objective OPF problems. A new formulation for the multi-objective optimal power flow (MOOPF) problem and DG unit allocation in the power system is also presented. The suggested approaches have been scrutinized and confirmed based on the IEEE 30-bus and 57-bus test systems. The requirement of the DG installation in the distributed system is to fulfill the power network operation necessities, generally to improve the total loss rises in the system. The DG units have to be allocated with optimal sizes in the network to reach maximum efficacy. A new meta-heuristic optimization technique called the Slime Mould Algorithm (SMA) approach has a high convergence rate or a few iterations and superior optimization indices analyzed against other algorithms. It can guarantee to enhance the efficiency of exploitation and exploration, based on a sustained balance between exploitation and exploration, to achieve promising statistical results. Therefore, the SMA method is redesigned for optimal location and sizing based on the total active power loss of the systems. And optimal results for single, two, and three DG allocation cases are obtained. The simulated results from the proposed DG-based SMA approach are also matched with the calculated solutions of the biogeography-based optimization (BBO) approach. The comparison and graphical analysis showed that the total active power loss, required iterations, percentage of the total loss minimization, and DG installed capacities are relatively improved using the suggested SMA algorithm based on the optimal DG unit sizing and location problem in the power systems. Secondly, a novel and competent meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is designed to handle various convex and non-linear, single-objective OPF problems. The HFPSO technique hybridizes the Firefly Optimization (FFO) algorithm and the Particle Swarm Optimization (PSO) method to improve the exploitation and exploration strategies and enhance the convergence rate. Moreover, the achieved results revealed the efficacy of the suggested HFPSO algorithm considering the acceptable convergence rate. The statistical examination demonstrated that the proposed method is a reliable and robust optimization approach to deal with OPF problems. Thus, evaluating the applicability and performance of the HFPSO algorithm, it is evident that the proposed method provides a better tool to solve OPF problems of electric power networks. Finally, a crowding distance and non-dominated-sorting-based multi-objective hybrid firefly & particle swarm optimization (MOHFPSO) algorithm is designed for MOOPF problems. The proposed algorithm is simulated for simultaneous OPF-based conflicting objectives, respectively. Besides, the approach's acquired optimized results are also compared against the simulated original OPF-based MOPSO method and the optimal values of the present literature work to authenticate its effectiveness. Comparing and analyzing the resultant optimal values indicated the proposed MOHFPSO method's dominance in the optimal solution. Consequently, the proposed algorithm with a non-dominated sorting approach can be efficiently applied for small and large power networks.