Multi-objective And Multi-area Optimization Of Hydrothermal Dynamic Environmental Economic Dispatch Using Hybridized Bat Algorithm

Abstract

Multi-Objective and Multi-Area Optimization of Hydrothermal Dynamic Environmental Economic Dispatch using Hybridized Bat Algorithm Economic Dispatch (ED) is one of the most important aspects of power systems planning and operation which must be considered in a Multi Area Power System for utilities in electricity exchange agreements to reap the benefits of system interconnections. This thesis presents a new formulation for ED problem called Multi Objective, Multi Area Hydrothermal Dynamic Environmental Economic Dispatch (MOMAHDEED) problem which determines the hourly optimal generating levels of all the hydro and thermal generating units in a multi area system to adequately supply the varying area load demands, such that the total fuel cost of thermal plants in all areas and emissions are simultaneously curtailed while satisfying physical and operational constraints. The multi objective functions in MOMAHDEED problem are combined using weighted sum method and optimal solutions are selected using cardinal priority ranking. MOMAHDEED is then solved using Hybridized Modified Bat Algorithm (HMBA) which is a new algorithm developed by modifying Bat Algorithm (BA) and hybridizing it with Differential Evolution (DE). The Bat Algorithm is a metaheuristic algorithm which is inspired by echolocation behavior of micro bats. HMBA is developed by modifying the velocity and frequency equations of BA to improve its exploitation and exploration capability and further hybridizing it using the Differential Evolution (DE) to increase its accuracy. The effectiveness and capability of the HMBA is tested by solving the Multi Area Environmental Economic Dispatch (MAEED) problem for four - area multi - area systems consisting of twelve and sixteen generating units. The scalability of HMBA is tested by solving the dynamic MOMAHDEED problem for a larger multi area system consisting of four areas, each with six generating units of larger capacities considering the effects of valve point loading, varying nature of demand and stochastic nature of water availability. HMBA realizes lower fuel costs and lower emissions compared to traditional BA and Particle Swarm Optimization (PSO) and its variants, Teaching – Learning Based Optimization (TLBO) and Pareto – Based Chemical- Reaction Optimization (PCRO) Algorithm for the same systems. Keywords: Bat Algorithm (BA), Differential Evolution (DE), Economic Dispatch (ED), Hybridized Modified Bat Algorithm (HMBA), Multi Objective, Multi Area Hydrothermal Dynamic Environmental Economic Dispatch (MOMAHDEED).