Relating two data sets at the row level where table one may correlate to zero or more rows in another
I would like to predict the monetary impact that an outage will have on the electric grid.


It may help to think of your problem as two problems:


Given an outage (or set of outages), predict the number of constraints that will result
Given a constraint, predict the monetary impact


You would need to build two regressive models - one for each of the problems.  It's difficult to give advice about which specific techniques or algorithms to use without a better understanding of the domain and of your dataset.

It sounds like you have a set of timestamped historical outages and a set of timestamped historical constraints.  To me, your description implied that there was no authoritative mapping between outages and constraints, so the first wrinkle may be assigning blame for each outage to one or more constraints.  This likely requires domain knowledge that most SO contributors won't have.

Another wrinkle is that you only have date and time information about outages and constraints.  If the temporal information explains a lot of the variance in number of outages and cost of those outages, then no problem.  Otherwise, it's going to be difficult to build accurate models.

If you go with the approach I suggested above, then you have a timeseries forecasting problem on your hands.  Traditional algorithms for this type of problem include auto-regressive models like ARMA, ARIMA, SARIMA, etc.  You can read up on those to see if they seem suitable for your problem.

Alternatively, Facebook recently released an open-source forecasting library Prophet designed to be easy-to-use and to work well out-of-the-box.  It's super easy to try out, so you could give it a shot.