Enhancing decision making in power system planning using observable Markov Models and multi-objective optimization

Abstract

In Power System planning, we seek to identify all current problems as well as potential issues in the electrical network and prioritize such cases accordingly for further action by management. Traditionally, load flow analysis is a network modeling and simulation approach that uses peak load as input data and generates feeder characteristics such as losses and voltages. The terminal voltages and losses obtained from such analysis are functions of the loading and circuit impedance. Since circuit impedance is fixed for an existing feeder, the peak loading becomes the main decision variable for System Planners. The use of peak loading alone for load flows therefore becomes an issue in Decision Making when an expectation of the peak is not quantified. In this research work, we apply Markov modeling to model the randomness in electric load behavior based on temperature. Instead of using just one historical peak loading, an expectation of the peak loading in different temperature states is modeled and used as the input for load flow studies. Based on the Decision Maker's (DM's) preferences and objectives for characteristics of the feeder in different states, the dominance test is used to rank feeders and their states. The approach sets the stage for Decision Making under uncertainty in power system planning.