MaOTLBO: Many-objective teaching-learning-based optimizer for control and monitoring the optimal power flow of modern power systems

Abstract

This paper recommends a new Many-Objective Teaching-Learning-Based Optimizer (MaOTLBO) to handle the Many-Objective Optimal Power Flow (MaO-OPF) problem of modern complex power systems while meeting different operating constraints. A reference point-based mechanism is utilized in the basic version of Teacher Learning-Based Optimizer (TLBO) to formulate the MaOTLBO algorithm and directly applied to DTLZ test benchmark functions with 5, 7, 10-objectives and IEEE-30 bus power system with six different objective functions, namely the minimization of the voltage magnitude deviation, total fuel cost, voltage stability indicator, total emission, active power loss, and reactive power loss. The results obtained from the MaOTLBO optimizer are compared with the well-known standard many-objective algorithms, such as the Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) and Non-Dominated Sorting Genetic Algorithm-version-III (NSGA-III) presented in the literature. The results show the ability of the proposed MaOTLBO to solve the MaO-OPF problem in terms of convergence, coverage, and well-Spread Pareto optimal solutions. The experimental outcomes indicate that the suggested MaOTLBO gives improved individual output and compromised solutions than MOEA/D-DRA and NSGA-III algorithms.

Integrating economic load dispatch information into the blockchain smart contracts based on the fractional-order swarming optimizer

“…Decentralized optimal power flow (D-OPF) in distribution networks represents a cutting-edge approach to enhancing the efficiency and reliability of power distribution, and integrating blockchain technology into this framework further elevates its capabilities. The traditional power flow management systems often face challenges in handling the increasing complexity of modern distribution networks with diverse energy sources (AlSkaif and Van Leeuwen, 2019;Ullah et al, 2022;Younesi et al, 2022;Jangir et al, 2023;Nappu et al, 2023). D-OPF, by distributing decision-making processes across the network nodes, allows for real-time optimization of power flows, ensuring optimal utilization of resources and minimization of losses such as reduction in fuel generation cost, active power line losses, overall operational costs, improved network voltage profile, security, and stability.…”

Four Directed Search Algorithm: A New Optimization Method and Its Hyper Strategy Investigation

“…In transportation systems, the classic variable neighborhood search is used to optimize the routing of the electric vehicle where the speed is timedependent, and the time window is soft [8]. In the energy sector, teaching learning-based optimization (TLBO) is used to optimize the power flow in the complex power system [9].…”

Multiobjective trajectory optimization of the wall‐building robot based on RBF–NSGA‐II in an uncertain viscoelastic contact environment

“…They can effectively solve practical problems of nonlinear systems and be used for practical production. Many multiobjective optimization algorithms have also been proposed (Jangir & Jangir, 2018; Jangir et al, 2017, 2021, 2023; Premkumar et al, 2021), and they are used in many fields, such as construction, manufacturing, control, decision making, prediction, and so forth. These algorithms can complete the work with the best results under the given constraints or limitations and obtain feasible solutions with minimum use of resources.…”

“…Many multiobjective optimization algorithms have also been proposed T A B L E 1 Characteristics of various viscoelastic models. (Jangir & Jangir, 2018;Jangir et al, 2017Jangir et al, , 2021Jangir et al, , 2023Premkumar et al, 2021), and they are used in many fields, such as construction, manufacturing, control, decision making, prediction, and so forth. (4) The proposed trajectory optimization method is compared and analyzed with the standard gate-shaped trajectory planning method, and the superiority of the method is verified.…”