  Distributed energy resources (DERs) such as grid-responsive loads and
batteries can be harnessed to provide ramping and regulation services across
the grid. This paper concerns the problem of optimal allocation of different
classes of DERs, where each class is an aggregation of similar DERs, to balance
net-demand forecasts. The resulting resource allocation problem is solved using
model-predictive control (MPC) that utilizes a rolling sequence of finite
time-horizon constrained optimizations. This is based on the concept that we
have more accurate estimates of the load forecast in the short term, so each
optimization in the rolling sequence of optimization problems uses more
accurate short term load forecasts while ensuring satisfaction of capacity and
dynamical constraints. Simulations demonstrate that the MPC solution can indeed
reduce the ramping required from bulk generation, while mitigating near-real
time grid disturbances.
