  We develop a structure-preserving parametric model reduction approach for
linearized swing equations where parametrization corresponds to variations in
operating conditions. We employ a global basis approach to develop the
parametric reduced model in which we concatenate the local bases obtained via
$\mathcal{H}_2$-based interpolatory model reduction. The residue of the
underlying dynamics corresponding to the simple pole at zero varies with the
parameters. Therefore, to have bounded $\mathcal{H}_2$ and $\mathcal{H}_\infty$
errors, the reduced model residue for the pole at zero should match the
original one over the entire parameter domain. Our framework achieves this goal
by enriching the global basis based on a residue analysis. The effectiveness of
the proposed method is illustrated through two numerical examples.
