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

Abstract

IntroductionDespite the global agenda of increasing the share of renewable energy production, thermal power plants contribute predominantly to the global electricity production and will continue to do so in the foreseeable future. This warrants a global concern to lower their operating costs. Allocating power generated in these plants in the least possible operating cost while meeting the system constraints has been one of the main concerns of the utility operators globally. To address this, engineers use the economic dispatch (ED) formulations which is a practical power system optimization problem.Because of the non-linearity, non-convexity and the multimodal characteristics present in the cost function of the ED, adopting a metaheuristic optimization technique (which is the state-of-the-art global optimization technique [1]) has two major advantages over the usage of the conventional techniques. Firstly, metaheuristic optimization techniques (MOTs) lead to better problem modelling that reduce assumptions related to problem characterization in terms of nonlinearity. Secondly, MOTs have better ability to obtain optimal solutions as compared with a conventional technique. Both of these aspects lead to optimal generator loadings for least cost operation within the power system. As a result, system operators can benefit from significant cost savings over the years.Earlier formulations of the ED problem were tackled using coventional mathematical techniques such as interior point method, lambda-iteration method and linear programming [2]. However, those methods cannot solve the ED problem when formulated in a non-linear context and they suffer from "curse of dimensionality" problems. In recent times, the knowledge growth of MOTs has given an opportunity to optimize the ED problem in a more practical way than the mathematical techniques. [9] and Glowworm Swarm Optimization (GWO) [10]. However, many of these techniques and their variants have shown a lack of ability to obtain consistent and robust optimal results reducing their effectiveness in a practical operation.

A new 2-D multi-stable chaotic attractor and its MultiSim electronic circuit design

“…Engineering fields have many utilizations of chaotic attractors [2]. Some common utilizations are enlisted such as oscillations [5], [6], vibrations [7], neuron models [8], [9], control and memristor models [10]- [12], mechanical attractors [13], [14].…”

“…Bahoo and Poria [13] used active control method for food chain model. Mustafa et al [14] used chaos-enhanced cuckoo search for economic dispatch with valve point effects. Vaidyanathan and Rasappan [15] used active control for the hybrid synchronization of hyperchaotic Qi and Lü systems.…”

An Effectively Modified Firefly Algorithm for Economic Load Dispatch Problem

“…However, these methods also own the same disadvantages such as limited applicability for problems with complicated constraints and less efficiency for large scale problem. To avoid overlapping such drawbacks of deterministic methods, many meta heuristic approaches have been constructed to deal with the ELD problem such as teaching and learning based algorithm (TLBA) [1], cuckoo search algorithm (CSA) [2][3][11][12], hybrid real coded genetic algorithm (HRCGA) [4], differential evolution (DE) [5], genetic Algorithm (GA) [5], particel swarm optimization (PSO) [5], improved evolutionary programming (IEP) [6], clonal algorithm (CA) [7], hybrid integer coded differential evolution (HICDE) [8], bee colony optimization (BCO) [13], biogeography-based optimization (BBO) [14], chaotic jaya algorithm (CJA) [15], firefly algorithm (FA) [16],memetic firefly algorithm (MFA) [16], FA with adaptive parameter α (ASPFA) [16], modified firefly algorithm (α-MFA) [17], seeker optimization algorithm (SOA) [18], krill herd algorithm (KHA) without using genetic operation (KHA1) [19], KHA using crossover operation (KHA2) [19], KHA using mutation operation (KHA3) [19] and KHA using crossover operation and mutation operation (KHA4) [19]. These methods have shown their powerful search ability via testing on different problems with different conditions such as different constraints related to thermal units and different constraints associate with power systems.…”