An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network

Abstract

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.

Hybrid PSO-TS Based Distribution System Expansion Planning for System Performance Improvement Considering Energy Management

“…A hybrid PSO-TS algorithm is presented in the paper which provides higher quality results in large scale optimization problems. A similar approach is presented in [20] for the substation and feeder expansion purpose. However, to the authors' best knowledge, for the first time, this research uses a hybrid PSO and TS algorithm for the distribution system expansion problem using different ERs and considering different sets of swarms for selecting the best results.…”

A Practical Large-Scale Distribution Network Planning Model Based on Elite Ant-Q

“…Equation (10) guarantees that each load node can be supplied by one group of feeders only instead of multiple groups of feeders. Equation (11) guarantees the continuity of the planned connection, i.e. the number of feeders with load node j as the first node is the same as that with load node j as the last node.…”

“…At the same time, Tabu search table is introduced to record visited nodes to ensure that each load node is connected by only one feeder. Thus, the solution of each agent automatically satisfies the topological constraints (9) (10) (11), which greatly simplifies the large-scale complex optimization problems.…”

“…However, the solution time increases greatly when involving a large number of discrete variables. Due to the little dependence on models, heuristic algorithms are widely employed in distribution network planning [9]- [11]. In recent years, a few scholars have used explicit expressions to calculate the reliability of the specific distribution network, and used mathematical methods (such as MIP, MILP algorithms) to carry out distribution network planning research [12], [13].…”

Settings-Free Hybrid Metaheuristic General Optimization Methods

“…Hybridization is a well-known strategy that boosts the capacity of optimization algorithms. Since a metaheuristic optimization algorithm cannot overcome all algorithms in solving any problem, hybridization can be a solution that merges the capabilities of different algorithms in one system [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52]. Many of these algorithms require the correct setting of control parameters, and merging several of these algorithms into a single solution increases the complexity of accurate adjustment of control parameters.…”