  This paper describes a control approach for large-scale electricity networks,
with the goal of efficiently coordinating distributed generators to balance
unexpected load variations with respect to nominal forecasts. To mitigate the
difficulties due to the size of the problem, the proposed methodology is
divided in two steps. First, the network is partitioned into clusters, composed
of several dispatchable and non dispatchable generators, storage systems, and
loads. A clustering algorithm is designed with the aim of obtaining clusters
with the following characteristics: (i) they must be compact, keeping the
distance between generators and loads as small as possible; (ii) they must be
able to internally balance load variations to the maximum possible extent. Once
the network clustering has been completed, a two layer control system is
designed. At the lower layer, a local Model Predictive Controller is associated
to each cluster for managing the available generation and storage elements to
compensate local load variations. If the local sources are not sufficient to
balance the cluster's load variations, a power request is sent to the
supervisory layer, which optimally distributes additional resources available
from the other clusters of the network. To enhance the scalability of the
approach, the supervisor is implemented relying on a fully distributed
optimization algorithm. The IEEE 118-bus system is used to test the proposed
design procedure in a non trivial scenario.
