  The arrival of small-scale distributed energy generation in the future smart
grid has led to the emergence of so-called prosumers, who can both consume as
well as produce energy. By using local generation from renewable energy
resources, the stress on power generation and supply system can be
significantly reduced during high demand periods. However, this also creates a
significant challenge for conventional power plants that suddenly need to ramp
up quickly when the renewable energy drops off. In this paper, we propose an
energy consumption scheduling problem for prosumers to minimize the peak ramp
of the system. The optimal schedule of prosumers can be obtained by solving the
centralized optimization problem. However, due to the privacy concerns and the
distributed topology of the power system, the centralized design is difficult
to implement in practice. Therefore, we propose the distributed algorithms to
efficiently solve the centralized problem using the alternating direction
method of multiplier (ADMM), in which each prosumer independently schedules its
energy consumption profile. The simulation results demonstrate the convergence
performance of the proposed algorithms as well as the capability of our model
in reducing the peak ramp of the system.
