An Emission-Aware Day-Ahead power scheduling system for Internet of Energy

• Main Authors: Chin-Ting Chen, 陳致廷
• Other Authors: Chenn-Jung Huang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/7wc8jt

碩士 === 國立東華大學 === 資訊工程學系 === 107 === The rapid development of emerging technologies and significant cost reductions offered by the utilization of solar energy and wind power have made it feasible to replace traditional power generation methods with renewable energy sources in the future. However, one thing that distinguishes renewables from currently deployed centralized power sources is that the former are categorized as intermittent energy sources. What's more, the scale of renewables is relatively small and their deployment could be described as scattered. In the recent literature, the architecture of the Internet of Energy has been proposed to replace the current smart grid in the future. However, the large volume of energy produced, the copious amounts of accompanying consumption data, and the uncertainty of the arrival times of electric vehicles and the intermittence nature of the renewable energy will result in the short-term energy management of the IoE in the future being much complicated. In this work, a hierarchical day-ahead power scheduling system based on the architecture of the IoE is proposed to tackle these complex energy management problems. Excess electricity generated in a microgrid is allocated to other microgrids facing power supply shortages, whereby the maximal usage of distributed renewables and a reduction of the burden on traditional power generation during time periods of peak load can be achieved. Flexible charging mechanism of moving electric vehicles is also considered in the proposed scheduling system to provide preferred charging options for moving EVs and flatten the load profile simultaneously. The experimental results show that the hierarchical day-ahead power scheduling system proposed in this work not only achieves emission reduction and balances peak and off-peak period loads in an electricity market, but also shortens the time overhead required for charging of moving EVs effectively.