Economic and emission dispatch using cuckoo search algorithm

The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. This paper proposes the use of Cuckoo Search Algorithm (CSA) for solving the economic and Emission dispatch. The effectiveness of the proposed approach has been tested on 3 generator system. CSA is a new meta-heuristic optimization method inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species.The results shows that performance of the proposed approach reveal the efficiently and robustness when compared results of other optimization algorithms reported in literatur

Chaos-Enhanced Cuckoo Search for Economic Dispatch with Valve Point Effects

Economic dispatch determines the optimal generation outputs to minimize the toal fuel cost while satisfying the load demand and operational constraints. Modern optimization techniques fail to solve the problem in a robust manner and finding robust global optimization techniques is necessary for efficient system operation. In this study, the potentiality of introducing chaos into the standard Cuckoo Search (CS) in order to further enhance its global search ability is investigated. Deterministic chaotic maps are random-based techniques that can provide a balanced exploration and exploitation searches for the algorithm. Four different variants are generated by carefully choosing four different locations (within the standard CS) with potential adoption of a candidate chaotic map.Then detailed studies are carried out on benchmark power system problems with four different locations to find out the most efficient one. The best of all test cases generated is chosen and compared with algorithms presented in the literature. The results show that the proposed method with the proposed chaotic map outperforms standard CS. Additionally, the chaos-enhanced CS has a very good performance in comparison with QPSO and NSS

Bat Algorithm: Literature Review and Applications

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.Comment: 10 page

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

A cuckoo search optimization scheme for non-convex economic load dispatch

This paper presents a Cuckoo Search (CS) based algorithm to solve constrained economic load dispatch (ELD) problems. The proposed methodology easily deals with non-smoothness of cost function arising due to the use of valve point effects. The performance of the algorithm has been tested on systems possessing 13 and 40 generating units involving varying degrees of complexity. The findings affirm that the method outperforms the existing techniques, and can be a promising alternative approach for solving the ELD problems in practical power system

Economic load dispatch solutions considering multiple fuels for thermal units and generation cost of wind turbines

In this paper, economic load dispatch (ELD) problem is solved by applying a suggested improved particle swarm optimization (IPSO) for reaching the lowest total power generation cost from wind farms (WFs) and thermal units (TUs). The suggested IPSO is the modified version of Particle swarm optimization (PSO) by changing velocity and position updates. The five best solutions are employed to replace the so-far best position of each particle in velocity update mechanism and the five best solutions are used to replace previous position of each particle in position update. In addition, constriction factor is also used in the suggested IPSO. PSO, constriction factor-based PSO (CFPSO) and bat optimization algorithm (BOA) are also run for comparisons. Two systems are used to run the four methods. The first system is comprised of nine TUs with multiple fuels and one wind farm. The second system is comprised of eight TUs with multiple fuels and two WFs. From the comparisons of results, IPSO is much more powerful than three others and it can find optimal power generation with the lowest total power generation cost

Hybrid method for solving the non smooth cost function economic dispatch problem

This article is focused on hybrid method for solving the non-smooth cost function economic dispatch problem. The techniques were divided into two parts according to: the incremental cost rates are used to find the initial solution and bee colony optimization is used to find the optimal solution. The constraints of economic dispatch are power losses, load demand and practical operation constraints of generators. To verify the performance of the proposed algorithm, it is operated by the simulation on the MATLAB program and tests three case studies; three, six and thirteen generator units which compared to particle swarm optimization, cuckoo search algorithm, bat algorithm, firefly algorithm and bee colony optimization. The results show that the proposed algorithm is able to obtain higher quality solution efficiently than the others methods