Abstract
Recent technological advances in the area of microgrids, such as the utilization of renewable generation resources to improve generated energy surety and economic gain, and advanced automated control systems; have created significant interest in the concept of self-healing power grids. Given the rise in the need to harvest and distribute energy in a secure, reliable, and low-cost fashion, self-healing microgrid technology has become a very promising form of electricity distribution. Self-healing microgrids and their control look especially promising in confined areas such as university campuses and city centers. As renewable energy costs continue to fall and fossil fuel costs continue to rise, microgrids are expected to be simultaneously developed to a higher extent and to operate in a wider range of areas, while still being connected to the existing main power grid infrastructure. However, the large voltage differential and centralized control strategies employed by most utility companies impede the implementation of microgrids in a networked configuration and limit their effectiveness to urban sites. The present work provides solutions for these issues through a Dynamic Data Driven Adaptive Multiscale Simulation (DDDAMS) framework that has been developed to provide distributed microgrids with a self-healing protocol both when they are operating collaboratively and competitively (in an isolated mode) while increasing the reliability of the network by pledging energy surety. In the present DDDAMS framework, an analytical protocol is built to guide the considered microgrids’ responses in the event of emergencies such as severe weather phenomena, damaged infrastructure, or earthquakes. The DDDAMS framework has been applied to a realistic case study that includes three microgrids and has been tested under four different emergency incidents. The results have shown that the cooperative collection of distributed microgrids was able to meet the essential and priority loads to a higher extent than standard methods at all times while limiting the effects on sacrificing from the less important non-essential loads, hence accomplishing a significantly improved management of energy resources.