Hybrid optimization algorithm to solve the nonconvex multiarea economic dispatch problem

In this paper, multiarea economic dispatch (MAED) problems are solved by a novel straightforward process. The solved MAED problems include transmission losses, tie-line constraints, multiple fuels, valve-point effects, and prohibited operating zones in which small, medium, and large scale test systems are involved. The methodology of tackling the problems consists in a new hybrid combination of JAYA and TLBO algorithms simultaneously to take the advantages of both to solve even nonsmooth and nonconvex MAED problems. In addition, a new and simple process is used to tackle with the interaction between areas. The objective is to economically supply demanded loads in all areas while satisfying all of the constraints. Indeed, by combining JAYA and TLBO algorithms, the convergence speed and the robustness have been improved. The computational results on small, medium, and large-scale test systems indicate the effectiveness of our proposed algorithm in terms of accuracy, robustness, and convergence speed. The obtained results of the proposed JAYA-TLBO algorithm are compared with those obtained from ten well-known algorithms. The results depict the capability of the proposed JAYA-TLBO based approach to provide a better solution.fi=vertaisarvioitu|en=peerReviewed

Comparisional Investigation of Load Dispatch Solutions with TLBO

This paper discusses economic load dispatch Problem is modeled with non-convex functions. These are problem are not solvable using a convex optimization techniques. So there is a need for using a heuristic method. Among such methods Teaching and Learning Based Optimization (TLBO) is a recently known algorithm and showed promising results. This paper utilized this algorithm to provide load dispatch solutions. Comparisons of this solution with other standard algorithms like Particle Swarm Optimization (PSO), Differential Evolution (DE) and Harmony Search Algorithm (HSA). This proposed algorithm is applied to solve the load dispatch problem for 6 unit and 10 unit test systems along with the other algorithms. This comparisional investigation explored various merits of TLBO with respect to PSO, DE, and HAS in the field economic load dispatch

Application Of Multi-Layered Perceptron Neural Network (MLPNN) With Consideration Losses And Emission Dispatch

Nowadays, optimum generation and consumption of energy are considered as two important problems. Economic Dispatch that is the most optimum is one of important problems in power system. This paper presents a multi-layered perceptron neural network (MLPNN) method to solve the combined Losses and emission dispatch problem. Therefore, knowing Problem-Solving in Economic Dispatch is a necessity. First, the Economic Dispatch is explained and then neural network is reviewed. Next, different kinds of neural networks are mentioned by using in Economic Dispatch

Application Of Multi-Layered Perceptron Neural Network (MLPNN) With Consideration Losses And Emission Dispatch

Nowadays, optimum generation and consumption of energy are considered as two important problems. Economic Dispatch that is the most optimum is one of important problems in power system. This paper presents a multi-layered perceptron neural network (MLPNN) method to solve the combined Losses and emission dispatch problem. Therefore, knowing Problem-Solving in Economic Dispatch is a necessity. First, the Economic Dispatch is explained and then neural network is reviewed. Next, different kinds of neural networks are mentioned by using in Economic Dispatch

Economic Load Dispatch using IYSGA

The Economic Load Dispatch (ELD) problem is a pivotal aspect of power system management, focusing on the efficient allocation of power generation among various units to meet the demand while minimizing costs. This research paper presents an Improved Yellow Saddle Goat Fish Algorithm (IYSGA) based method for resolving ELD issues. The key objective of proposed IYSGA method is to reduce error between demanded and generated load along with its unit cost. This objective is accomplished by using YSGA whose exploration ability is improved by exploring ability of Grasshopper Optimization Algorithm (GOA). By implementing IYSGA in given ELD problem, the convergence rate, exploring ability and solution quality is enhanced. The fitness function is determined by IYSGA in terms of error and cost reduction, which should be as minimum as possible. The simulations are performed on standardized IEEE bus system with 3-unit and 6-units to meet load demand of 850MW to 1263MW respectively. The experimental simulations conducted provide evidence that the proposed approach met the load demand with zero error. Furthermore, proposed method attained best cost of $8197.633 and$15,285.7055 for the 3-unit and 6-unit generation unit. These outcomes underscore the robustness and superiority of the proposed method in addressing the Economic Load Dispatch (ELD) problem, emphasizing its capacity to optimize power generation with unparalleled precision and cost-effectiveness.&nbsp