On stochastic optimal bidding strategy for microgrids

In this paper, we addressed the issue of a stochastic optimal bidding problem for a system with microgrids (MGs). The optimal bidding problem is formulated as a two-stage stochastic programming process, which aims to minimize the system operation cost and to expand energy interactions among local MGs that are geographically close. Uncertainties come from both energy supply and demand sides (e.g., wind, solar, and load demand) are considered in the stochastic model and random parameters to represent those uncertainties are captured by using the Monte Carlo method. To enable an optimal electricity trading between local MGs, we presented two bidding schemes: (i) Cournot equilibrium based Dynamic Backtrack Energy Trading (DBET), and (ii) double auction based Dual Decomposition Auction (DDA). Experimental results on an IEEE-33 bus based system with MGs were presented to show the effectiveness of our proposed schemes. Experimental results show that our proposed bidding schemes can reduce the operation cost of the system, while the DDA scheme achieves better performance in terms of system social welfare than the DBET scheme.