ToU pricing model based on DGs in microgrids

• Main Author: Wang, Zhe
• Other Authors: So Ping Lam
• Format: Theses and Dissertations
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/65185
• Institution: Nanyang Technological University

The objective of this dissertation is to design a novel time varying, half an hour based electricity tariff for end customers of a microgrid. With the depletion of conventional energy sources and higher requirement for a clean and sustainable generation, a clear trend towards distributed generations, and an emphasis on power quality and system stability has spurred a shift toward a more decentralized, distributed power grid. The high penetration on renewable energy sources in a microgrid has also caused a new problem. All of the renewable energy sources are intermittent and uncontrollable to some extent, that is, peak power generation is not unnecessarily comes with the peak demand and it is also impossible to fix their power generation to the rated power. Thus, considerable capacity and energy storages system are needed to ensure the reliability of the system. However, during the off-peak period when load demand is low, these capacities is in idle, which will cause an unfavorable waste of resources and increase the initial capital investment. In order to reduce the peak electricity demand and grant the demand with ability to track and follow the power generation curve, a novel ToU pricing scheme is designed by using model predictive control (MPC). In order to implement the MPC algorithm, power generation and load forecasting models are studied. The developed models are programmed using Nl LabVIEW and simulated with history data collected from solar array installed on the rooftop of School of Electrical and Electronic Engineering (EEE) building and Wee Kim Wee School of Communication and Information (WKWSCI) building in Nanyang Technological University accordingly. The power generation model and load forecasting model are integrated into the MPC algorithm for developing the ToU pricing model. The developed ToU pricing model is programmed and simulated using NI Lab VIEW. The simulation results show that the proposed ToU pricing scheme can efficiently reduce the peak electricity demand and grant the load demand curve with the ability of track and follow the power generation curve.