A two-stage two-layer optimization approach for economic operation of a microgrid under a planned outage

Microgrids (MGs) are a promising and viable solution for sustainable cities and community development. However, it is challenging to achieve the economic operation of MGs due to the uncertainties of generation, load demand, and outage occurrence. Consequently, this paper proposes a novel two-stage two-layer optimization approach to minimize the total operation cost of an MG under a high dimension of uncertainties of generation, load demand, and planned outages. In the upper layer, the binary chance-constrained (CC) for outage planning under the n-1 criterion is developed considering the outage uncertainty. Then, the CC for optimal day-ahead scheduling is introduced to deal with the uncertainties of generation and load demand. Since the prediction error means are not perfectly known, we propose an efficient second order cone formulation via the distributionally robust optimization method to solve the MG economic operation problem. The lower layer performs real-time optimization to compensate for the power deviation and determines a fair benefit sharing for the use of the external devices. To validate the proposed approach, we conducted simulations with different uncertainty levels and outage scenarios. The simulation results illustrate the effectiveness of the proposed approach, resulting in a remarkable operational cost reduction.