Solution of Non-Convex and Dynamic Economic Load Dispatch Problem of Small Scale Power Systems using Dragonfly Algorithm Vikram Kumar Kamboj Department of Electrical Engineering, DAV University, Jalandhar, Punjab, India Ashutosh Bhadoria Department of Electrical Engineering, DAV University, Jalandhar, Punjab, India Pawanpreet Singh M.Tech Research Scholar, Electrical Engineering Department, DAV University, Jalandhar S. K. Bath Department of Electrical Engineering, GZS Campus College of Engineering & Technology, Bathinda, Punjab Abstract—Dragonfly algorithm is a novel intelligence optimization technique, which simulates the static and dynamic swarming behaviours of dragonflies in environment. Exploration and exploitation in dragonfly algorithm is achieved by modelling the social interaction of dragonflies in navigating, searching for foods and avoiding enemies when swarming dynamically or statistically. This paper presents the application of dragonfly algorithm for the solution of non-convex and dynamic economic load dispatch problem of electric power system. The performance of dragonfly algorithm is tested for economic load dispatch problem of six IEEE benchmarks of small scale power systems and the results are verified by a comparative study with Lambda Iteration Method, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Simulated Annealing( SA), Artificial Bee Colony (ABC), Evolutionary Programming (EP) and Grey Wolf Optimizer(GWO). Comparative results show that the performance of Dragonfly algorithm is better than recently developed GWO algorithm and other well known heuristics and meta-heuristics search algorithms. Keywords— Economic Load Dispatch Problem (ELDP), Dragonfly Algorithm (DA), Grey Wolf Optimizer (GWO) I. INTRODUCTION In modern power system networks, there are various generating resources like thermal, hydro, nuclear etc. Also, the load demand varies during a day and attains different peak values. Thus, it is required to decide which generating unit to turn on and at what time it is needed in the power system network and also the sequence in which the units must be shut down keeping in mind the cost effectiveness of turning on and shutting down of respective units. The entire process of computing and making these decisions is known as unit commitment (UC). The unit which is decided or scheduled to be connected to the power system network, as and when required, is known to be committed unit. Unit commitment in power systems refers to the problem of determining the on/off states of generating units that minimize the operating cost for a given time horizon. Electrical power plays a pivotal role in the modern world to satisfy various needs. It is therefore very important that the electrical power generated is transmitted and distributed efficiently in order to satisfy the power requirement. Electrical power is generated in several ways. The most significant crisis in the planning and operation of electric power generation system is the effective scheduling of all generators in a system to meet the required demand. The Economic Load Dispatch (ELD) problem is the most important optimization problem in scheduling the generation among thermal generating units in power system. Economic dispatch in electric power system refers to the short-term discernment of the optimal generation output of various electric utilities, to meet the system load demand, at the minimum possible cost, subject to various system and operating constraints viz. operational and transmission constraints. The Economic Load Dispatch Problem (ELDP) means that the electric utilities (i.e. generator's) real and reactive power are allowed to vary within certain limits so as to meet a particular load demand within lowest fuel cost. The ultimate aim of the ELD problem is to minimize the operation cost of the power generation system, while supplying the required power demanded. In addition to this, the various operational constraints of the system should also be satisfied. The problem of ELD is usually multimodal, discontinuous and highly nonlinear. Although the cost curve of thermal generating units are generally modelled as a smooth curve, the input-output characteristics are nonlinear by nature because of valve-point loading effects, Prohibited Operating Zones (POZ), ramp rate limits etc. In recent years, various evolutionary, heuristic and meta- heuristics optimization algorithms have been developed simulating natural phenomena such as: Genetic Algorithm(GA) [1], Ant Colony Optimization (ACO) [2], Particle Swarm Optimization[3], Simulating Annealing(SA)[4], Gravitational Local Search (GLSA) [5], Big-Bang Big-Crunch (BBBC) [6], Gravitational Search Algorithm (GSA) [7], Curved Space Optimization (CSO) [8], Charged System Search (CSS) [9], Central Force Optimization (CFO) [10], Artificial Chemical Reaction Optimization Algorithm (ACROA) [11], Black Hole (BH) International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Published by, www.ijert.org ACMEE - 2016 Conference Proceedings Volume 4, Issue 15 Special Issue - 2016