Application of Soft Computing Techniques for Renewable Energy Network Design and Optimization

Abstract

Energy operation can be characterized by its complex network system composed of energy generation, energy transformation, energy transportation, and energy consumption. The network has provided the great flexibility for energy transformation and transportation; meanwhile, it presents a complex task for conducting agile energy dispatching when extreme events have caused local energy shortages that need to timely be restored. One of the useful methodologies to solve such a problem is soft computing, which conducts collaboration, association, and complementariness of the different techniques that integrates them. The applications and developments of soft computing have amazingly evolved in the last two decades. Many of these applications can be found in the field of renewable energy and energy efficiency where soft computing techniques are showing a great potential to solve the problems that arise in this area. In this chapter, several soft computing techniques are briefly introduced. Then the methodology framework and implementation procedures are presented to demonstrate the application of artificial neural networks and curve fitting for renewable energy network design and optimization, which has the capability to handle restoration during extreme and emergency situations with uncertain parameters.