A Day-Ahead Renewables-Based Power Scheduling System for Internet of Energy

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 of electric vehicles and the intermittence of the renewable energy will result in the short-term energy management of the IoE in the future being much more complicated than the energy management of traditional power generation systems which still rely on centralized-control. We thus propose a day-ahead power scheduling system based on the architecture of the IoE to tackle these complex energy management problems. The whole power system is divided into different geographical regions under a hierarchical framework. The microgrids first collect electricity consumption data from smart appliances used in households and data pertaining to the power generating capacity of renewable energy sources at the microgrid level. Then, the regional energy routers schedule the usage of electricity for the customers by considering the efficiency of the use of distributed renewables and the battery storage systems. Notably, a reallocation mechanism is presented in this work to allow the energy routers to allocate excess electricity generated in a microgrid to others facing power supply shortages, whereby the maximal usage of distributed renewables and a reduction of the burden on some microgrids during time periods of peak load can be simultaneously achieved. The experimental results show that the hierarchical day-ahead power scheduling system proposed in this work can mitigate the dependency on traditional power plants effectively and balance peak and off-peak period loads in an electricity market.