Open AccessA novel binary variant model of swarm inspired polar bear optimization algorithm employed for scalable unit commitment
Author(s) -
Ul Haq Syed Sadam,
Ahmad Aftab,
Ikram Faiza,
Nawaz Tooba,
Majeed Ayesha,
Saddique Muhammad Shahzar,
Sattar Muhammad Kashif
Publication year - 2021
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12711
Subject(s) - mathematical optimization , power system simulation , computer science , particle swarm optimization , scalability , population , metaheuristic , swarm behaviour , electric power system , algorithm , power (physics) , mathematics , physics , quantum mechanics , demography , database , sociology
Summary For an effective and reliable power system, determining the optimal state of the accessible producing units is the key step, which comprises of two segments; making decision in committing the generators (unit commitment) followed by the decision‐making of their active power allocation (economic dispatch). Unit commitment (UC) problem is a mixed‐integer bit transition‐state optimization problem to which classical optimizing tools strive to evaluate, whereas metaheuristic methods prove to be a healthy attempt. In this study, a binary model of the polar bear optimization (BPBO) algorithm is developed and is utilized to solve this combinatorial problem. Polar bear optimization (PBO) is a newly developed swarm‐based approach, simulating three mechanisms in one technique in the form of effectual exploration and exploitation phase along with dynamic control of population size. BPBO uses a modified sigmoid conversion function to produce the state bits (ON/OFF) of the units. These three potent procedures help in determining the optimal transition state of units by varying the size of the search agents. For solving the economic dispatch problem (EDP), the conventional lambda iteration method is used. Repairing of the infeasible bit is also carried out by applying heuristic adjustments. To illustrate the vitality of the BPBO, it is modeled in “MATLAB 2016a” and tested on IEEE 4, 5, 10, 20, 40, 60, 80, and 100‐unit test systems. Comparative evaluation in terms of optimum combinations, active power allocation, and simulation time with other available methodologies is also done, resulting in a progressive approach with superiority to other recent methods.