Solving practical power system problems using hierarchical interactive PSO strategy considering SVC controllers

Abstract

This paper presents a hierarchical interactive particle swarm optimization (HIPSO) for solving the static multi objective optimal power flow (OPF) considering multi SVC controllers and dynamic economic dispatch (DED). This study introduces a variant of PSO using new mechanism search based dynamic adjustment of PSO parameters coordinated with a hierarchical strategy for reactive power planning stage to improve the performances of the original PSO in terms of solution quality and convergence characteristics. The HIPSO algorithm optimizes a combined control variables such as: active power generations, voltage control of generators, tap transformer and the reactive power of multi SVC Controllers installed at a specified buses to minimize fuel cost, total power loss and voltage deviation while satisfying the generators constraints and power system security. The proposed approach examined and applied to many practical test systems: IEEE 57-Bus, IEEE 118-Bus, 40 generating units and 10 generating units to solving; the static multi objective OPF with quadratic fuel cost function, the economic dispatch considering valve point effects and the dynamic economic dispatch considering both valve point effects and ramp rate limits. Simulation results compared to Matpower, to the standard PSO and others recent metaheuristic methods confirm the effectiveness of the proposed combined approach.plex when practical constraints related to generating units (valve point effect, ramp rate limits) and also when FACTS devices are integrated as non-linear control variables [2]. In recent years many types of FACTS Controllers FACTS; shunt Controllers (SVC, STAT-COM), series Controllers (TCSC, SSSC) and hybrid controllers (UPFC) modeled and integrated in the electricity market to improve the performances of power system planning and control [3,4]. The role of FACTS technology is not only to bring a system under control and to transmit power as ordered by the control centers, but also to increase the usable transmission capacity to its thermal limits [5]. Various mathematical methods have been used largely for solving the OPF

Interactive gravitational search algorithm and pattern search algorithms for practical dynamic economic dispatch

“…Figures show convergence performance for 40 units test system at three stages: first trail without external communication, the second and the fourth trail with internal and external communication. The best results of the proposed approach compared with other recent methods cited in are depicted in Table , which shows clearly the efficiency of the proposed approach. It is important to note that the near global solution achieved at a reduced number of trails, for this test case only 10 trails are sufficient to achieve the near optimal solution.…”

“…This is a larger and complex system with many local optima. The system data can be retrieved from . Figures show convergence performance for 40 units test system at three stages: first trail without external communication, the second and the fourth trail with internal and external communication.…”

“…The losses are ignored in this case for sake of comparison, and the load demand is considered over 24‐hour time interval, ramp rate limits, power generation limits, and load demand are given in Tables and . The optimal generation dispatch found using HIPSO and the combined GSA–PS are presented in Tables and .…”

“…Many variants based evolutionary algorithm and hybrid methods have been proposed and applied with success to solving practical DED considering valve point effect and prohibited zones, like deterministically guided PSO , a modified hybrid EP–SQP , quantum genetic algorithm (QGA) , artificial bee colony optimization , artificial bee colony coordinated with sequential quadratic programming , adaptive particle swarm optimization (APSO) , artificial immune system (AIS) , an improved PSO (IPSO) , improved chaotic particle swarm optimization (ICPSO) , an adaptive hybrid differential evolution (AHDE) , a hybrid multiagent based particle swarm optimization (HMPSO) , enhanced cross‐entropy method (ECEM) , biogeography based optimization method (BOP) , hierarchical interactive PSO strategy , hybrid GA–DE–PS method , improved GA with non‐stationary penalty functions , A hybrid FCASO–SQP method , A fuzzy adaptive chaotic ant swarm optimization , a hybrid CPSO–SQP method , hybrid bare‐bones PSO , and a feasible operation region for DED and reserve monitoring is proposed in . These recent hybrid methods have a better searching ability in finding near global optimal solution particularly for large and complex problems compared to the mathematical methods and the standard evolutionary algorithms.…”