1. An Energy Distribution Model Analysis Using Smart Grid Solution Md Amanullah Kabir, Jiang Zhou   Department of Mechanical Engineering, Lamar University Beaumont, TX,
Abstract
This research focuses on the efficient as well as economical electric distribution system within the context of regulatory governance. Based on the game theory with detailed payoff functions a potential model is developed which considers both of general users and smart grid users in a region. In this model, smart grid users act as the electric distributor. The model is developed with the consideration of the following different factors- no of users having renewable sources, a possibility of major electric breakdown, government involvement in smart distribution. A smart controller (PID) is used for the proper distribution which can determine the smart distribution of electricity by considering different parameters.
1. Research Motivation
To develop a sequential, cost-effective electric distribution system which can help the smart grid users economically.
To investigate the role of governance in interactions between distributors and consumers in electric distribution systems, representing both household and factory as a potential distributor.
To study and modeling an energy distribution system considering smart grid users by defining solar energy as a main potential energy resource.
To develop and examine a smart meter which can calculate the net energy and respective amount and its opportunity.
2. Methodology
In this project, a model was constructed by considering smart grid users as a potential energy distributor. The Model includes following steps:
Identification of no of smart grid users and potential renewable energy source
Installation of Transmission and distribution system
Controlling system Installation
Smart meter design
Fig. 2 Energy Distribution Using Game Theory
Fig. 3 Step Up Transformer
Fig. 4 Controller Design and Analysis
Fig. 4 Title of Figure here
Fig. 1 Modelling
Proceedings of the 2018 ASEE Gulf-Southwest Section Annual Conference The University of Texas at Austin
April 4-6, 2018