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A new Bayesian approach to linear system identification
has been proposed in a series of recent papers. The
main idea is to frame linear system identification as predictor
estimation in an infinite dimensional space, with the aid of
regularization/Bayesian techniques. This approach guarantees
the identification of stable predictors based on the prediction
error minimization. Unluckily, the stability of the predictors
does not guarantee the stability of the impulse response of
the system. In this paper we propose and compare various
techniques to guarantee that the final model identified following
this Bayesian approach is stable. First, we consider the socalled
“LMI - constraint” approach and adapt it to constrain
the eigenvalues of the estimated model within the unit circle.
A second possibility which is being considered is to add to the
“classic” Stable-Spline algorithm a penalty term, depending on
the maximum absolute value of the eigenvalue of the system.
This last technique has the advantage of being integrated
directly inside the pre-existing optimization problem and not to
simply post-process the estimated model to guarantee stability.
Finally, we considered a Monte Carlo Markov Chain approach
sampling in both the space of hyper-parameters and of impulse
responses. Simulations results comparing these techniques will
be provided.

We revisit the classic slow coherency and area
aggregation approach to model reduction in power networks.
The slow coherency approach is based on identifying sparsely
and densely connected areas of a network, within which
all generators swing coherently. A time-scale separation and
singular perturbation analysis then results in a reduced loworder
system, where coherent areas are collapsed into aggregate
variables. Here, we study the application of slow coherency
and area aggregation to first-order consensus systems and
second-order power system swing dynamics. We unify different
theoretic approaches and ideas found throughout the literature,
we relax some technical assumptions, and we extend existing
results. In particular, we provide a complete analysis of the
second-order swing dynamics – without restrictive assumptions
on the system damping. Moreover, we identify the reduced
aggregate models as generalized first or second-order Laplacian
flows with multiple time constants, aggregate damping and
inertia matrices, and possibly adverse interactions.

The integration of renewable energy sources in power systems requires a well-balanced
control to guarantee system stability in view of fast
uctuating power injections. We present
several conditions on the stability reserve of a power system in terms of the region of attraction
of its steady state that can be used to design and evaluate such controllers. The power system
is modeled by coupled swing equations. The region of attraction of this nonlinear systems is
determined based on Lyapunov theory and Barbalat's lemma. The resulting conditions provide
both 2-norm and 1-norm regions of attractions. The dierent conditions dier, e.g., in their
conservatism and in the required knowledge of the power system parameters.

In this paper we address the problem of exploitingthe distributed energy resources (DER) available in a smartmicro-grid to minimize the power distribution losses via optimalreactive power compensation. Due to their typically smallsize, the amount of reactive power provided by each micro-generator is subject to tight saturation constraints. As aconsequence, it might be impossible to achieve convergence tothe global optimum based on algorithms that rely on short-range, gossip-type communication. We therefore propose arandomized multi-hop protocol that guarantees convergence ofthe distributed optimization algorithm also when only short-range communications are possible, at the expense of someadditional communication overhead.