A Gradient-Based Optimizer with a Crossover Operator for Distribution Static VAR Compensator (D-SVC) Sizing and Placement in Electrical Systems

Abstract

A gradient-based optimizer (GBO) is a recently inspired meta-heuristic technique centered on Newton’s gradient-based approach. In this paper, an advanced developed version of the GBO is merged with a crossover operator (GBOC) to enhance the diversity of the created solutions. The merged crossover operator causes the solutions in the next generation to be more random. The proposed GBOC maintains the original Gradient Search Rule (GSR) and Local Escaping Operator (LEO). The GSR directs the search to potential areas and aids in its convergence to the optimal answer, while the LEO aids the searching process in avoiding local optima. The proposed GBOC technique is employed to optimally place and size the distribution static VAR compensator (D-SVC), one of the distribution flexible AC transmission devices (D-FACTS). It is developed to maximize the yearly energy savings via power losses concerning simultaneously different levels of the peak, average, and light loadings. Its relevance is tested on three distribution systems of IEEE 33, 69, and 118 nodes. Based on the proposed GBOC, the outputs of the D-SVCs are optimally varying with the loading level. Furthermore, their installed ratings are handled as an additional constraint relating to two compensation levels of 50% and 75% of the total reactive power load to reflect a financial installation limit. The simulation applications of the proposed GBOC declare great economic savings in yearly energy losses for the three distribution systems with increasing compensation levels and iterations compared to the initial case. In addition, the effectiveness of the proposed GBOC is demonstrated compared to several techniques, such as the original GBO, the salp swarm algorithm, the dwarf mongoose algorithm, differential evolution, and honey badger optimization.

Optimal Integration of Multiple D-SVCs for Voltage Stability Enhancement in Radial Electrical Distribution System Using Adaptive Firefly Algorithm

“…In case 1, the VAr compensation limit is defined as 50% of total reactive power demand of the network and whereas in case 2, it is taken as 75%, respectively. From [20], the results of gradient-based optimizer (GBO), GBO with crossover operator (GBOC), dwarf mongoose optimization algorithm (DMOA), salp swarm algorithm (SSA), differential evolution (DE), bernstein-levy search DE (BSDE) and honey badger algorithm (HBA) are compared with the proposed AFA.…”

“…This is more than optimal VAr compensation and treated as over compensation. The best sizes of D-SVCs by AFA in ± kVAr (bus #) are as follows: 308 (22), 818 (61) and 221 (20). The total real and reactive power losses reduced to 𝑃 𝑙𝑜𝑠𝑠(𝑏𝑎𝑠𝑒) = 148.405 kW and 69.24 kVAr, respectively.…”

“…In [19], the adaptive differential search algorithm (ADSA) is utilised for solving the SVC location and sizes, along with active power DGs for ensuring minimum distribution losses. In [20], a gradient-based optimizer with a crossover operator (GBOC) is introduced for solving D-SVCs in RDN for techno-economic benefits. Further, different approaches to reactive power compensation in EDNs via conventional approaches and D-FACTS can be reviewed in [21].…”

A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power Systems

“…Therefore, researchers have attempted to develop metaheuristic methods to sidestep the issues that traditional methods possess. There are diverse population-based heuristics that are used to solve the OPFI, such as the electromagnetism-like mechanism [ 37 ], simulated annealing optimization [ 38 ], Particle Swarm Optimization (PSO) [ 39 ], Gradient-Based Optimization Algorithm (GBOA) [ 40 ], and Quantum computing with Moth Flame Technique (QMFT) [ 41 ]. In addition, in [ 42 ], the TLBO technique has been developed and adopted for solving the allocation optimization problem in power systems of capacitors for the sake of power factor correction.…”

Parameter Identification of Solar Photovoltaic Systems Using an Augmented Subtraction-Average-Based Optimizer

“…Each year, hundreds of algorithms for optimization are proposed, adopted, and used in the domains of power system engineering, such as hydro generation scheduling [30], improving the power system operation [31], wind power uncertainty in distribution systems [32] optimal reactive-power dispatch [33], static Var compensator devices applications [34] and Efficiency Improvement of Distribution Systems [32]. Recently, a technique named SABA [35] has been presented where its fundamental premise is to update population members' locations in the search space by deducting the average of searcher agents.…”