Development of Meta Heuristics Algorithm for Solving Multi Objective Economic and Emission Dispatch Problems

Abstract

ABSTRACT newlineEconomic Dispatch is an imperative optimization problem in the field of power systems that tries to achieve the objective of dividing the demand for power among online generators in an economic manner, while satisfying discreate constraints at the same time. Since the cost of power generation is exorbitant, an optimum dispatch saves a appreciable amount of money. Traditional algorithms like the Lambda Iteration, the Base Point Participation Factor, the Gradient Method and the Newton Method can solve the Economic Dispatch problem effectively, if and only if the fuel cost curves of the generating units are piece-wise linear and monotonically increasing. In recent years, the Economic Dispatch problem has taken a new twist, as people have become increasingly concerned about environmental issues. As a result, the Economic Dispatch now appeals to the dispatch of systems to minimize pollutants, as well as to acquire the minimum cost. The multi-objective generation dispatch in electric power systems treats the economic and the emission impacts as competing objectives, which require some reasonable trade-offs among objectives in order to reach an optimal solution. The Optimum Power Flow (OPF) solution is also the most important when it comes to power system planners and operators. The main goal is to find the optimal settings of a given power system network that optimizes a selected objective function, such as satisfying its power flow equations and equipment operating limits. Due to fuel costs escalating world-wide, increasing demand for electricity, and a growing concern for the environment, power utilities strive for optimal economic operation of their electric networks. A balance between profitable energy choices and environmentally-friendly practices is the main goal of this thesis. In this thesis, the Economic and Emission dispatch problem with valve point loading are optimized that are simultaneously composed by the load flow analysis for a six bus and IEEE 30 bus system. Two modern meta-heuristic optimiza