Enhanced Teaching Learning-Based Algorithm for Fuel Costs and Losses Minimization in AC-DC Systems

Abstract

The Teaching Learning-Based Algorithm (TLBA) is a powerful and effective optimization approach. TLBA mimics the teaching-learning process in a classroom, where TLBA’s iterative computing process is separated into two phases, unlike standard evolutionary algorithms and swarm intelligence algorithms, and each phase conducts an iterative learning operation. Advanced technologies of Voltage Source Converters (VSCs) enable greater active and reactive power regulation in these networks. Various objectives are addressed for optimal energy management, with the goal of attaining economic and technical advantages by decreasing overall production fuel costs and transmission power losses in AC-DC transmission networks. In this paper, the TLBA is applied for various sorts of nonlinear and multimodal functioning of hybrid alternating current (AC) and multi-terminal direct current (DC) power grids. The proposed TLBA is evaluated on modified IEEE 30-bus and IEEE 57-bus AC-DC networks and compared to other published methods in the literature. Numerical results demonstrate that the proposed TLBA has great effectiveness and robustness indices over the others. Economically, the reduction percentages of 13.84 and 21.94% are achieved for the IEEE 30-bus and IEEE 57-bus test systems when the fuel costs are minimized. Technically, significant improvement in the transmission power losses with reduction 28.01% and 69.83%, are found for the IEEE 30-bus and IEEE 57-bus test system compared to the initial case. Nevertheless, TLBA has faster convergence, higher quality for the final optimal solution, and more power for escaping from convergence to local optima compared to other published methods in the literature.

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

“…In addition, regarding the control variables, the limitations for the output powers from generators, generators voltage, reactive powers injection from Var sources, and tap settings are handled using Equations (10)–(13), respectively [ 56 , 57 ]. where Ngen represents the total number of generation units; Nt illustrates the total number of transformer units; Nq characterizes the total number of compensating/capacitors units; Pgen illustrates generators’ real power output; Tp denotes the tap changer settings; Vgen gives voltages of the generators; Qc signifies the reactive power injections of switching reactors/capacitors.…”

Optimal Reactive Power Flow of AC-DC Power System with Shunt Capacitors Using Backtracking Search Algorithm

“…In [34], the Songo-Apollo link was used to network model with DIgSILENT Power Factory software (made by DIgSILENT GmbH in Gomaringen, Germany) in a system with HVDC and flexible alternating current technology systems (FACTS). In [35], the Teaching Learning-Based Algorithm (TLBA) was used to reduce both system power losses and fuel costs.…”