A Solution to Economic Dispatch Problem using Hybrid Particle Chemical Reaction Optimization (HP-CRO) Algorithm

The electrical control request is on stagger due to constant growth in entreaty since manufacturing firms besides ménage. Consequently augmented plus price operational power cohort is the prerequisite of the era. In power classification process plus scheduling the Economic Dispatch (ED) tricky is prevailing and noteworthy some. The ED problematic of several standards is disentangled through consequently sundry conventional and meta-heuristics aggrandizement skills. Now the triumphed investigation exertion, a unique plus innovative aggrandizement way, Chemical Reaction Optimization (CRO) mongrelised thru Particle Swarm Optimization (PSO) system is recommended aimed at the explanation of ED problematic. Generating unit power limits, load fulfillment and prohibited operating zones remain painstaking by way of guarded on behalf of diverse IEEE customary ED muddles. The non-convex economic dispatch hitch is coded at Hybrid Particle Chemical Reaction Optimization (HP-CRO) contrivance then selected the MATLAB environment intended for 50 prosecutions, 50 search agents and 1000 iterations. The offered scheme stands smeared proceeding a number of IEEE usual trial structures counting six, fifteen plus forty engendering parts. The displayed upshots stay coordinated per quantified wont declared hip the articles aimed at directly above trial structures. The costs obtained commencing wished-for modus operandi bounce a superiority toward individuals gained after new practise on one occasion price opinion.

Multi-Fuel Power Dispatch Considering Prohibited Operating Zones and Tie-Line Flow Limits Using Ant Lion Optimizer

The electrical power generation from fossil fuel releases several contaminants into the air, and these become excrescent if the generating unit is fed by multiple fuel sources (MFS). The ever more stringent environmental regulations have forced the utilities to produce electricity at the cheapest price and the minimum level of pollutant emissions. The restriction in generator operations increases the complexity in plant operations. The cost effective and environmental responsive operations in MFS environment can be recognized as a multi-objective constrained optimization problem. The ant lion optimizer (ALO) has been chosen as an optimization tool for solving the MFS dispatch problems. The fuzzy decision-making mechanism is integrated in the search process of ALO to fetch the best compromise solution (BCS). The intended algorithm is implemented on the standard test systems considering the prevailing operational constraints such as valve-point loadings, CO2 emission, prohibited operating zones and tie-line flow limits.

The Nomadic People Optimizer applied to the economic dispatch problem with prohibited operating zones

This work proposes the application of the Nomadic People Optimizer (NPO) to solve the economic dispatch problem considering Prohibitive Operating Zones (POZ). The NPO is a swarm-based metaheuristic recently introduced in the literature and still under-explored. In addition, the POZ increase the difficulties to find the optimal solution of the economic dispatch problem. The performance of the proposed methodology is compared with others metaheuristics present in the literature. Also, a sensibility analysis was performed. The NPO performed better than Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) metaheuristics in solving the problem.

Teaching-Learning-Based Optimization Algorithm for the Combined Dynamic Economic Environmental Dispatch Problem

The Dynamic Economic Environmental Dispatch Problem (DEEDP) is a major issue in power system control. It aims to find the optimum schedule of the power output of thermal units in order to meet the required load at the lowest cost and emission of harmful gases. Several constraints, such as generation limits, valve point loading effects, prohibited operating zones, and ramp rate limits, can be considered. In this paper, a method based on Teaching-Learning-Based Optimization (TLBO) is proposed for dealing with the DEEDP problem where all aforementioned constraints are considered. To investigate the effectiveness of the proposed method for solving this discontinuous and nonlinear problem, the ten-unit system under four cases is used. The obtained results are compared with those obtained by other metaheuristic techniques. The comparison of the simulation results shows that the proposed technique has good performance.

Non-convex constrained economic power dispatch with prohibited operating zones and piecewise quadratic cost functions

This paper is focused on the solution of the non-convex economic power dispatch problem with piecewise quadratic cost functions and practical operation constraints of generation units. The constraints of the economic dispatch problem are power balance constraint, generation limits constraint, prohibited operating zones and transmission power losses. To solve this problem, a meta-heuristic optimization algorithm named crow search algorithm is proposed. A constraint handling technique is also implemented to satisfy the constraints effectively. For the verification of the effectiveness and the superiority of the proposed algorithm, it is tested on 6-unit, 10-unit and 15-unit test systems. The simulation results and statistical analysis show the efficiency of the proposed algorithm. Also, the results confirm the superiority and the high-quality solutions of the proposed algorithm when compared to the other reported algorithms.

Chaotic butterfly optimization algorithm applied to Multi objective Economic and Emission dispatch in modern power system

Aims : To optimize the economic and emission dispatch of the thermal power plant. Background: Considering both the economic and environmental aspects, a combined approach had made to attain a solution is known as the combined economic and emission dispatch problem. The CEED problem is a nonlinear bi-objective problem with conflicting behaviour with all the practical constraints. Objective: A new optimization method is improvised by applying the chaotic mapping to the butterfly optimization algorithm. This method is applied to the Combined Economic and Emission Dispatch (CEED) problem for optimizing consumed fuel cost and produced environment pollutant. Methods: Improved Chaotic Butterfly algorithm is applied to the optimization problem to optimize combined economic and emission dispatch. Result : The proposed technique is tested for four different test systems with various practical constraints like valve point loading, ramp rate limit and prohibited operating zones. The obtained results from the chaotic butterfly optimization algorithm (CBOA) are compared with other optimization techniques provide an optimum solution for CEED problem. Conclusion : Considering the environmental impact the novel metaheuristic swarm intelligence technique is applied with conflict of interest. Different test systems, with different practical operational constraints like valve-point loading, prohibited operating zones and ramp rate limits and emission dispatch have been analyzed to validate the implementation of the proposed algorithm in real life CEED problem situations.