Optimal Bidding Strategy for Microgrids Considering Renewable Energy and Building Thermal Dynamics

In this paper, we study an optimal day-ahead price-based power scheduling problem for a community-scale microgrid (MG). The proposed optimization framework aims to balance between maximizing the expected benefit of the MG in the deregulated electricity market and minimizing the MG operation cost considering users' thermal comfort requirements and other system constraints. The power scheduling and bidding problem is formulated as a two-stage stochastic program where various system uncertainties are captured by using the Monte Carlo simulation approach. Our formulation is novel in that it can exploit the thermal dynamic characteristics of buildings to compensate for the variable and intermittent nature of renewable energy resources and enables us to achieve desirable tradeoffs for different conflicting design objectives. Extensive numerical results are presented to demonstrate the great benefits in exploiting the building thermal dynamics and the flexibility of the proposed scheduling method in achieving different practical design tradeoffs. We also investigate the impacts of different design and system parameters on the curtailment of renewable energy resources and the optimal expected profit of the MG.