DISTRIBUTED DEMAND-SIDE OPTIMIZATION WITH LOAD UNCERTAINTY

Demand-side management will play a crucial role in balancing the energy generation and demand in future smart grids. In this paper, game-theoretic demand-side management algorithms are proposed for energy consumption scheduling under load uncertainty. The demand-side optimization and scheduling problem is formulated as a noncooperative cost minimization game among the end-users and an iterative algorithm that averages over the load uncertainty is proposed for solving it. The proposed algorithm is proven to converge to a Nash equilibrium. Simulation results show that taking into account the uncertainty in the load reduces significantly the load peak-to-average ratio and the hourly variation of the aggregate load profile.