Chaos Firefly Algorithm With Self-Adaptation Mutation Mechanism for Solving Large-Scale Economic Dispatch With Valve-Point Effects and Multiple Fuel Options

This paper presents a new metaheuristic optimization algorithm, the firefly algorithm (FA), and an enhanced version of it, called chaos mutation FA (CMFA), for solving power economic dispatch problems while considering various power constraints, such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options. The algorithm is enhanced by adding a new mutation strategy using self-adaptation parameter selection while replacing the parameters with fixed values. The proposed algorithm is also enhanced by a self-adaptation mechanism that avoids challenges associated with tuning the algorithm parameters directed against characteristics of the optimization problem to be solved. The effectiveness of the CMFA method to solve economic dispatch problems with high nonlinearities is demonstrated using five classic test power systems. The solutions obtained are compared with the results of the original algorithm and several methods of optimization proposed in the previous literature. The high performance of the CMFA algorithm is demonstrated by its ability to achieve search solution quality and reliability, which reflected in minimum total cost, convergence speed, and consistency

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

Antlion optimization algorithm for optimal non-smooth economic load dispatch

This paper presents applications of Antlion optimization algorithm (ALO) for handling optimal economic load dispatch (OELD) problems. Electricity generation cost minimization by controlling power output of all available generating units is a major goal of the problem. ALO is a metaheuristic algorithm based on the hunting process of Antlions. The effect of ALO is investigated by solving a 10-unit system. Each studied case has different objective function and complex level of restraints. Three test cases are employed and arranged according to the complex level in which the first one only considers multi fuel sources while the second case is more complicated by taking valve point loading effects into account. And, the third case is the highest challenge to ALO since the valve effects together with ramp rate limits, prohibited operating zones and spinning reserve constraints are taken into consideration. The comparisons of the result obtained by ALO and other ones indicate the ALO algorithm is more potential than most methods on the solution, the stabilization, and the convergence velocity. Therefore, the ALO method is an effective and promising tool for systems with multi fuel sources and considering complicated constraints

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

An alternative method to solve combined economic emission dispatch problems using flower pollination algorithm

Flower Pollination Algorithm (FPA) is a new biologically inspired meta-heuristic optimization technique based the pollination process of flowers. FPA mimics the flower pollination characteristics in order to survival by the fittest. This research presents implementation of FPA optimization in solving Combined Economic Emission Dispatch (CEED) problems in power system which minimize total generation cost by minimizing fuel cost and emission. Increasing in power demand requires effective solution to provide sufficient electricity to customer with minimum cost of operation at the same time considering emission. CEED actually is a multi-objective problem and need complex programming to solve it. The problem becomes complicated when there is practical constraints to be considered as well. To simplify the programming, objective of economic dispatch (ED) and emission dispatch (EmD) are combined into a single function by price penalty factor and analysed using weighted sum method to choose the best compromising result. In this research, the valve point loading effect problem in power system also will be considered. The proposed algorithm are tested on four different test systems which are: 6-generating unit and 11-generating unit without valve point effect with no transmission loss, 10-generating unit with having valve point effect and transmission loss, and lastly 40-generating unit with having valve point effect without transmission loss. The results of these four different test cases were compared with the optimization techniques reported in recent literature in order to observe the effectiveness of FPA. Result shows FPA able to perform better than other algorithms by having minimum fuel cost and emission

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