Risk-limiting Load Restoration for Resilience Enhancement with Intermittent Energy Resources

Microgrids are resources to restore critical loads after a natural disaster, enhancing the resiliency of a distribution network. To deal with stochastic power generated by intermittent energy resources within microgrids, such as wind turbines (WTs) and photovoltaics (PVs), most existing methods require forecast information. However, some microgrids may not be equipped with power forecasting tools. To fill this gap, a risk-limiting strategy based on real-time measurements is proposed. The Gaussian mixture model (GMM) is used to represent a prior joint distribution of power outputs of WTs and PVs over multiple periods based on historical data. As time rolls forward, with latest measurement data, the distributions of WT/PV generation are updated in a recursive manner. The updated distributions are used as inputs for load restoration, enabling an equivalent transformation of the original probabilistic-constrained problem to a mixed integer linear programming (MILP) that is computationally tractable. Simulation cases on a distribution system with three microgrids demonstrate the effectiveness of the proposed method. Results also indicate that networked microgrids have better uncertainty management capabilities than stand-alone microgrids.