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Expansion planning problems refer to the monetary and unit investment neededfor energy production or storage. An inherent element in these problems is theelement of stochasticity in various aspects, such as the generation output ofthe units, climate change or frequency and duration of grid outages. Especiallyfor the latter one, outage modeling is crucial to be carefully considered whendesigning systems with distributed generation at their core, such asmicrogrids. In most studies so far, a single statistical distribution is used,such as a Poisson Process. However, by taking a closer look at the real outagedata provided by the state of NY, it is observed that the outages do not seemto come from the same distribution. In some years, there is a huge spike in theaverage duration per outage and this is because of catastrophic events.Therefore, in this study we propose and test an alternative modeling for outageevents. This alternative scheme will be based on the premise that outages canbe broadly classified into two categories: regular and severe. Under thistaxonomy, it can still be assumed that each type of events follows a PoissonProcess but outages, in general, follow a Poisson Process which is truly asuperposition of these two types. A reinforcement learning approach is used tosolve the expansion planning problem and real location-specific data are used.The results verify our initial hypothesis and show that the optimizationresults are significantly affected by the outage modeling. To sum up, modelingaccurately the grid outage events and measuring directly the reliabilityperformance of an energy system during catastrophic failures could provideinvaluable tools and insights that could therefore be used for the bestpossible preparation for this type of outages.

The impact of analytical outage modeling on expansion planning problems in the area of power systems