Distribution expansion planning considering reliability and security of energy using modified PSO (Particle Swarm Optimization) algorithm

Abstract

Distribution feeders and substations need to provide additional capacity to serve the growing electrical demand of customers without compromising the reliability of the electrical networks. Also, more control devices, such as DG (Distributed Generation) units are being integrated into distribution feeders. Distribution networks were not planned to host these intermittent generation units before construction of the systems. Therefore, additional distribution facilities are needed to be planned and prepared for the future growth of the electrical demand as well as the increase of network hosting capacity by DG units. This paper presents a multiobjective optimization algorithm for the MDEP (Multi-Stage Distribution Expansion Planning) in the presence of DGs using nonlinear formulations. The objective functions of the MDEP consist of minimization of costs, END (Energy-Not-Distributed), active power losses and voltage stability index based on SCC (Short Circuit Capacity). A MPSO (modified Particle Swarm Optimization) algorithm is developed and used for this multiobjective MDEP optimization. In the proposed MPSO algorithm, a new mutation method is implemented to improve the global searching ability and restrain the premature convergence to local minima. The effectiveness of the proposed method is tested on a typical 33-bus test system and results are presented.

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

“…Besides, it can also escape local optimal solutions [32]. Aghaei et al [33] developed a modified PSO algorithm used for multiobjective optimization. In the proposed method, a new mutation method was performed to improve the global searching ability and restrained the premature convergence to local minima to achieve higher accuracy in electrical demand forecasting.…”

Multi-objective Truss Optimization Using Different Types of the BB-BC Algorithm

“…In order to overcome these drawbacks, we have implemented hybridization of BB-BC and some characteristics of Particle Swarm Optimization (PSO) proposed in [5] because this approach has been proven to be successful in solving different design problems [5,6]. PSO is optimization algorithm inspired by birds flocking or fish schooling in search for food [7,8], where every individual (particle) adjusts its movements according to both its own experience and the population experience. This feature has been used for modifying the BB-BC into hybrid BB-BC (HBB-BC) by using not only the center of mass from the previous generation (X c k ) but also the best position of a given individual solution up to iteration k (X lbest(k) ) and the best position in the whole population found up to the iteration k (X gbest(k) ) [5]:…”

“…( 1 (8) where n pf is the number of members in Pareto front and d i is the Euclidean distance (in the objective space) between the member i in Pareto front and its nearest member. A smaller value of S implies a more uniform distribution of solutions in Pareto front.…”

Stochastic Real-Time Scheduling of Wind-Thermal Generation Units in an Electric Utility

“…Also, it is observed form the numerical studies that by increasing the dimension of the system, more reduction in CPU time is obtained, which is very important from the view point of real-time operation of large-scale power systems. Future work will be focused on comparing the performance of the proposed method in comparison to the other existing methods like as Meta heuristic methods (Particle Swarm Optimization [31] and Honey Bee Mating Optimization (HBMO) [32]). The uncertainty of wind power generation as well as the demand uncertainty are considered using scenario approach.…”