Hybrid Discrete Evolutionary PSO for AC dynamic Transmission Expansion Planning

Abstract

Multiyear Transmission Expansion Planning (TEP) aims to determine how and when a transmission network capacity should be expanded taking into account an extended horizon. This is an optimization problem very difficult to solve and that has unique characteristics that increase its complexity such as its non-convex search space and its integer and nonlinear nature. This paper describes a hybrid tool to solve the TEP problem, including a first phase to select a list of equipment candidates conducted by a Constructive Heuristic Algorithm (CHA), and a second phase that uses Discrete Evolutionary Particle Swarm Optimization (DEPSO) for the final planning. Both phases use the AC power flow model as a way to improve the realism of the developed tool. The paper includes a case study based on the IEEE 24-Bus Reliability Test System and the results show that tools based on swarm intelligence applied to reduced search spaces are able to find good quality solutions with low computational effort.

Multiyear and multi-criteria AC Transmission Expansion Planning model considering reliability and investment costs

“…The system used in the tests has some differences regarding the original system proposed in [6] and the system details can be found in [3] and [4].…”

“…The NDCCGA used 50 individuals in the population and the parent circle radius was set at 4 10 . The parameters for the CHA and the Hill Climbing methods are the same as used in [5].…”

“…On the other hand, using a large number of branches can turn the computational effort to solve the TEP problem prohibitive. Therefore, the search space was reduced using a Constructive Heuristic Algorithm (CHA) detailed in [4].…”

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

“…The proposed technique worked well in achieving the Global Maximum Power Point (GMPP): simulation and experimental results verified this under different partial shading conditions and as such its reliability in tracking the global optima was established. Gomes and Saraiva [81] described a hybrid evolutionary tool to solve the Transmission Expansion Planning problem. The procedure is phased out in two parts: first equipment candidates are selected using a Constructive Heuristic Algorithm and second, a DEPSO optimizer is used for final planning.…”

“…A brief listing of some of the important hybrid algorithms using SA and PSO are given below in Table 2. [76] 2010 DEPSO Clustering Xiao and Zuo [77] 2012 Multi-DEPSO Dynamic Optimization Junfei et al [78] 2013 DEPSO Mobile Robot Localization Sahu et al [79] 2014 DEPSO PID Controller Seyedmahmoudian et al [80] 2015 DEPSO Photovoltaic Power Generation Gomes and Saraiva [81] 2016 DEPSO Transmission Expansion Planning Boonserm and Sitjongsataporn [82] 2017 DEPSO-Scout Numerical Optimization [86] demonstrated the efficiency of the proposed method. Chu et al [87] developed an Adaptive Simulated Annealing-Parallel Particle Swarm Optimization (ASA-PPSO).…”