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

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

Tianxing Cai (Lamar University, USA)
DOI: 10.4018/978-1-5225-3817-2.ch009
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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.
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Soft Computing Techniques

Soft computing is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally hard tasks such as the solution of NP-complete problems, for which there is no known algorithm that can compute an exact solution in polynomial time. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. In computer science and related fields, artificial neural networks (ANNs) are computational models inspired by animals' central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. These techniques have a very wide application (Ganesan, Elamvazuthi, Shaari, & Vasant, 2013; Elamvazuthi, Vasant & Ganesan, 2013; Vasant, 2013; Ganesan, Vasant, & Elamvazuthi, 2013; Ganesan, Elamvazuthi, Ku Shaari, & Vasant, 2013)

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