Multi-objective optimization application in power systems

Abstract

For hundreds of years, electricity supply industries have been in the hands of monopoly utilities, until engineers conceived the management of this industry as a challenging multi-objective optimization problem. Multiple objectives need to interact in divergent or competing interests to win the competition and to deliver the electrical energy and keep the lights on. The most highlighted objectives are the economic dispatch of power for satisfying consumers with lower power bill costs, the minimum loss in transmission lines, the environmental emission reduction, and the reliability and stability of the voltage and generated power. The consideration of mentioned goals push for a tighter coordination of planning and operation scheduling in a power system and raises interest in using multi-objective optimization methods in power system applications. The Optimal Power Flow (OPF) is used to determine an optimal operating condition for power systems while considering the limitations and system constraints such as: generator capability, line capacity, bus voltages, and power flow balances. Among many optimization methods, the Teaching-Learning Based Optimization (TLBO) algorithm has gained wide acceptance in different areas of science and engineering. In this study, the Teaching-Learning-Based Optimization algorithm is used to optimize the Optimal Power Flow problem considering total fuel cost of generation, emission, voltage deviation and, active power transmission losses in single and multi-objective cases. The method has been applied on the IEEE 30-bus and IEEE 57-bus test systems for several OPF objectives including minimization of fuel cost, emission, voltage deviation, and power losses, and the results reflect the effectiveness of the method.