Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

Abstract

This paper proposes a novel improved polar bear optimization (IPBO) algorithm and employs it along with polar bear optimization (PBO) and chaotic population-based variants of polar bear optimization algorithm to solve combined economic emission dispatch (CEED) problem. PBO is a meta-heuristic technique inspired by the hunting mechanisms of polar bears in harsh arctic regions based only on their sense of sight. Polar bears in nature exhibits hunting of prey not only on their sight but also on their keen sense of smell. Hence, a novel improved variant of PBO which enhances its operation by equipping it with tracking capabilities utilizing polar bears sense of smell has been proposed in this study. The validity of novel IPBO is tested through 5 benchmark functions and 140 units Korean ED problem. Furthermore, the impact of different population initialization methods is also observed on the capabilities of conventional PBO. The proposed chaotic population based PBO, improved PBO (IPBO) and PBO are employed to solve IEEE 3 unit and 6-unit CEED problem. CEED is a multi-objective power system optimization problem with conflicting objectives of cost and emission. The simulations performed undertake each objective individually as well as collectively. The results achieved by each technique are analyzed statistically through Wilcoxon rank sum test (WRST), probability density function and cumulative density function. Both the statistical and numerical analysis of results showcase the strength of each solution technique as well as their ability to improve cost and emissions in the solution of CEED problem.

SPSO Based Optimal Integration of DGs in Local Distribution Systems under Extreme Load Growth for Smart Cities

“…In References [10,[21][22][23][24][25], the authors implemented several approaches for optimal DGs location. In References [26,27], an analytical method based on generalized reduced gradient was used to an analytical method based on generalized reduced gradient was used to locate DGs optimally in a meshed network for maximum benefits.…”

“…Based on power injection, these are classified into four categories [23]: DGs, especially based on renewable energy resources (RERs), are more emerging than centralized power generation. When appropriately installed, DGs decline power loss considering power stability, voltage profile and environmental conditions [24,25]. So, optimal placement of DG is crucial; otherwise, it can further increase the power loss and originate more pronounced voltage fluctuations.…”

IOT Based Fault Detection and Protection of Power Transformer in the Smart Grid

“…Programming was performed in C language [12]. Differential protection was very important for all the devices in power systems [13].…”

Solution to Solid Wood Board Cutting Stock Problem

“…In terms of computational complexity, it is an NP-hard problem with the highest complexity; the solution time increases exponentially with the problem scale, and there is no accurate optimal solution in the polynomial time [14][15][16]. At present, most studies use heuristic methods to solve optimization problems, such as polar bear optimization [17][18][19][20], grey wolf optimizer [21][22][23], and genetic algorithm [24][25][26], which can find good approximate solutions in a limited time.…”

“…The one-dimensional cutting sto timization problem. In terms of computational complexity, it is the highest complexity; the solution time increases exponentia and there is no accurate optimal solution in the polynomial tim studies use heuristic methods to solve optimization problems, zation [17][18][19][20], grey wolf optimizer [21][22][23], and genetic algorit good approximate solutions in a limited time. The one-dimensional cutting stock problem has been stu Common layout methods include column generation, greedy optimization algorithms.…”