Two-layer stochastic day-ahead and real-time energy management of networked microgrids considering integration of renewable energy resources

Abstract

In this paper, we propose a multi-time scale energy management for the networked microgrids that considers both day-ahead and real-time scheduling. The proposed multi-time scale energy management system consists of two stages. In the first stage, a stochastic two-layer framework is implemented to minimize the operating costs of the networked microgrid in the day-ahead market. In the second stage, the first operation planning is updated in real-time every 15 min to reduce fluctuations in renewable energy sources. The objective function of the second stage is to minimize the cost of imbalance and optimally regulate the local generation of microgrids. Also, demand response programs have been used to reduce peak demand and the operating cost of MGs. Two case studies have been investigated to evaluate the impact of the proposed method. However, the effect of arbitrage in single and double price markets has been evaluated. The proposed model has been formulated as mixed-integer linear programming and the obtained results show that the proposed model reduces the operating cost by $86.27 and provides better planning by internal power trading.