Static and dynamic environmental economic dispatch using tournament selection based ant lion optimization algorithm

Abstract

The static and dynamic economic dispatch problems are solved by creating an enhanced version of ant lion optimisation (ALO), namely a tournament selection-based ant lion optimisation (TALO) method. The proposed algorithm is presented to solve the combined economic and emission dispatch (CEED) problem with considering the generator constraints such as ramp rate limits, valvepoint effects, prohibited operating zones and transmission loss. The proposed algorithm’s efficiency was tested using a 5-unit generating system in MATLAB R2021a during a 24-hour time span. When compared to previous optimization methods, the suggested TALO reduces the costs of fuel and pollution by 9.01 and 4.7 percent, respectively. Furthermore, statistical analysis supports the suggested TALO optimization superiority over other methods. It is observed that the renewable energy output can be stabilized in the future by combining a hybrid dynamic economic and emission dispatch model with thermal power units, wind turbines, solar and energy storage devices to achieve the balance between operational costs and pollutant emissions.

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

“…Many authors [6, 10, 12, 16-19, 22, 23, 27, 29, 31, 43] have compared algorithms based on tracking capability, total execution time, tracking efciency [16,29], and accuracy. Te authors in [25] compared in terms of population size, [14,24] compared in terms of Iteration. Te authors in [7] analysed the performance at diferent irradiation and PSC.…”

“…Te authors in [7] analysed the performance at diferent irradiation and PSC. Refrences [12,14,15,18,20,33] compared RMSE and, the authors in [20] in terms of MAE. In this paper, three cases, STC, stepchanging irradiation, and PSC, are taken to validate the performance of JFO in terms of maximum power tracking, total execution time, the minimum number of iterations to achieve the GMPP with high tracking efciency, and RMSE for the same set of population and number of iterations.…”

“…PSO [5,6,8,11,13,14,16,17,19,[23][24][25][26], GA [8,[11][12][13][14]23], and ABC [5,8,[11][12][13][16][17][18][19] are the most widely used optimization algorithms because of some advantages [5,16,17] such as their simple structure, independence of a mathematical model, capability to solve nonlinear and challenging mathematical problems, easy understanding, and high reliability. Each optimization technique considers a few deciding factors such as the number of populations and a few particles/food sources/chromosomes/jellyfsh.…”

“…A few of the well-known optimization techniques are artifcial immune system (AIS) [8], ant colony optimization (ACO) [5,8,[12][13][14], moth-fame optimization (MFO) [12,15], whale optimization algorithm (WOA) [12], butterfy optimization (BFO) algorithm [12], shufed frog leaping algorithms (SFLA) [12], slap swarm algorithm [12], frefy algorithm (FA) [12,16,17], artifcial bee colony (ABC) [5,8,9,12,13,[16][17][18][19][20], bat-search algorithm (BA) [12,17], grey wolf optimization (GWO) [5,12,13,15,17,21,22], genetic algorithm (GA) [8, 12-14, 21, 23], fower pollination optimization (FPO) [12,16,24], JAYA [16], DE [8,16], cuckoo search (CS) [5,6,12,13,15,16,…”

“…Te authors in [32] have given a detailed explanation of the jellyfsh optimization algorithm with its application in engineering. Te authors in [11,14] have shown a comparison of the diferent optimization techniques [28]. PSO and FPA are compared with ten (10) samples for PSC and it is concluded that the FPA produces 86.03 W power output compared to the power output in PSO of 85.5 W under PSC in the PV panel.…”