Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm †

Abstract

This paper presents an efficient approach for solving the optimal reactive power dispatch problem. It is a non-linear constrained optimization problem where two distinct objective functions are considered. The proposed approach is based on the hybridization of the particle swarm optimization method and the tabu-search technique. This hybrid approach is used to find control variable settings (i.e., generation bus voltages, transformer taps and shunt capacitor sizes) which minimize transmission active power losses and load bus voltage deviations. To validate the proposed hybrid method, the IEEE 30-bus system is considered for 12 and 19 control variables. The obtained results are compared with those obtained by particle swarm optimization and a tabu-search without hybridization and with other evolutionary algorithms reported in the literature.

A Frequency–Power Droop Coefficient Determination Method of Mixed Line-Commutated and Voltage-Sourced Converter Multi-Infeed, High-Voltage, Direct Current Systems: An Actual Case Study in Korea

“…However, conventional generators can be considered in the optimization problem with a little modification, because the generators can be represented as active and reactive power sources similar to the VSC HVDC system. Additionally, the security constraint of the conventional generators can be represented by simple inequality of active and reactive power [33].…”

Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms

“…Test Network References C1 C2 C3 C4 C5 C6 C7 C8 C9 OF1 X X -------38 bus-Roy-Billinton Test System [8] X -X -X X ---IEEE 30, 57, 118 and 300 bus [10,27,29] X -X -X ----IEEE 33 bus [13] X X X X -----IEEE 33 and 94 bus [14,15] X X X -X X ---IEEE 30 bus [21] X X X -X ----IEEE 33 and 85 bus [25,28] X X X X X ----IEEE 33 and 119 bus [5] OF2 X --------IEEE 10, 23 and 34 bus [12] X -X -X ----IEEE 22, 69, 85 and 141 bus [32] OF3 X -X -X X ---IEEE 30, 57, 118 and 300 bus [10,27] X X X -X X ---IEEE 30 bus [21] OF4 X -X ---X -X IEEE 10, 33 and 69 bus [16,17,22,26] X -X ----X -IEEE 10, 15 and 34 bus [19] X -X ------IEEE 30 and 85 bus [20] X -X ------IEEE 33, 34, 69 and 85 bus [23,27] X -X -X -X -X IEEE 85 and 118 bus [24] OF5 X ---X ----IEEE 28-bus [11] X --------IEEE 9-bus [30] OF6 X -X --X X --IEEE 30 bus [18] X -X -X X X --IEEE 30, 57 and 118 bus [27] X -X -X ----IEEE 30, 118 and 300 bus [29] Energies 2019, 12, 4239 4 of 36…”

“…For active power loss reduction using load flow computation, the branch and bound method is generally preferred, for its reduced computation time. For example, for the minimization of the total annual costs, the Crow Search Algorithm (CSA) is used in [16,17], the Particle Swarm Optimization (PSO) and hybrid PSO algorithm are adapted in [18][19][20][21], the Flower Pollination Algorithm (FPA) is preferred in [22,23], and an Improved Harmony Algorithm is chosen in [24]. On the other hand, the OCBA problem based on active power minimization was approached in [25,26] using the Bacterial Foraging Optimization Algorithm, the Intersect Mutation Differential Evolution (IMDE) Algorithm in [27], the Artificial Bee Colony (ABC) in [5,28] and the Ant Lion Optimization Algorithm in [29].…”

An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization

“…Many newly developed meta-heuristic algorithms like ant-lion optimizer [18], dragon fly optimization [19], hybrid particle swarm optimization-Tabu search (PSO-TS) [20], and hybrid artificial physics optimization-particle swarm optimization (APOPSO) [21] have been put forth to solve the RPD problem. Though all are found to be efficient methods to achieve the main objectives, none of them specifically calculate the discrete variables as discrete and integer variables as integer during the solution determination process.…”