Optimisation of smart grid comprising of renewable energy and electric vehicles using HOMER software and threshold decision method

• Main Author: Ho, Chang Fei.
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Final Year Project
• Language: English
• Published: 2012
• Subjects: DRNTU::Engineering::Mechanical engineering::Alternative, renewable energy sources
• Online Access: http://hdl.handle.net/10356/49408
• Institution: Nanyang Technological University

Heading towards a sustainable future has become one of the priorities in recent development of Singapore. In order to reduce the reliance on fossil fuels as the major source of energy, there is a need to optimise the current grid system by incorporating renewable energy. However, renewable energy poses several problems in a real-site implementation due to high costs involved in initial construction and the limitations of regional conditions including weather condition and geographical location. To overcome current difficulties on renewable energy implementation, the student needs to analyse the feasibility and cost effectiveness of implementing the renewable energy hybrid system. The student proposes the method of optimizing the smart grid system in Singapore comprising of various renewable energy sources using HOMER software. Another sustainability issue is transportation. Conventional vehicles run on petrol and emit greenhouse gases which worsen Global Warming. To create a sustainable future, there is a need to replace these vehicles with electric vehicles (EVs). However, the conventional uncontrolled charging method for EVs strained the network supply especially when the requirement of charging EVs is concentrated. Thus, smart charging scheme is introduced to allow control over every charging interval and optimization of grid system over whole charging period. For optimal charging of EVs, intelligent nodes and two way communication in real time are necessary. However, the smart charging scheme has a limitation due to the high uplink feedback overhead from the EVs. To solve this limitation of feedback overhead, the student proposes a threshold criteria scheme which determined the participation of the uplink signaling feedback to charge each EV. Charging commenced only for EVs below the threshold value. The threshold value gradually increased according to the variation of the battery state of charge level of EVs. The proposed scheme is able to maintain system performance and reduce the feedback signaling overhead significantly.