  As we transition towards a power grid that is increasingly based on renewable
resources like solar and wind, the intelligent control of distributed energy
resources (DER) including photovoltaic (PV) arrays, controllable loads, energy
storage and plug-in electric vehicles (EVs) will be critical to realizing a
power grid that can handle both the variability and unpredictability of
renewable energy sources as well as increasing system complexity. Realizing
such a decentralized and dynamic infrastructure will require the ability to
solve large scale problems in real-time with hundreds of thousands of DERs
simultaneously online. Because of the scale of the optimization problem, we use
an iterative distributed algorithm previously developed in our group to operate
each DER independently and autonomously within this environment. The algorithm
is deployed within a framework that allows the microgrid to dynamically adapt
to changes in the operating environment. Specifically, we consider a commercial
site equipped with on-site PV generation, partially curtailable load, EV charge
stations and a battery electric storage (BES) unit. The site operates as a
small microgrid that can participate in the wholesale market on the power grid.
We report results for simulations using real data that demonstrate the ability
of the optimization framework to respond dynamically in real-time to external
conditions while maintaining the functional requirements of all DERs.
