Proactive Resilient Scheduling for Networked Microgrids With Extreme Events

In this paper, we propose a risk-constrained adaptive robust optimization approach to provide proactive resilient scheduling decisions for multiple networked microgrid central controllers under potential extreme events. Our objective is to minimize both risks of false judgement made by microgrid central controllers and damage done to networked microgrids by extreme events through a proactive resilient scheduling process. A risk-constrained adaptive robust optimization approach is developed to handle risks and uncertainties associated with: (i) extreme events that may occur and contingency issues linked to influential buses; (ii) renewable energy sources power generation; (iii) human reactions when faced with an extreme event; and (iv) status of combined cooling, heat and power units. “Budget of uncertainty” and risk-management parameters are utilized together to overcome both overconservative issues of conventional robust optimization and human errors that may occur when making decisions. Extensive simulation results from real-world data sets show that the risk-constrained adaptive robust optimization approach we propose can ensure the resilience of networked microgrids under extreme events.