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With the increasing amount of distributed energy resources (DERs)integration, there is a significant need to model and analyze hosting capacity(HC) for future electric distribution grids. Hosting capacity analysis (HCA)examines the amount of DERs that can be safely integrated into the grid and isa challenging task in full generality because there are many possibleintegration of DERs in foresight. That is, there are numerous extreme pointsbetween feasible and infeasible sets. Moreover, HC depends on multiple factorssuch as (a) adoption patterns of DERs that depend on socio-economic behaviorsand (b) how DERs are controlled and managed. These two factors are intrinsic tothe problem space because not all integration of DERs may be centrally planned,and could largely change our understanding about HC. This paper addresses theresearch gap by capturing the two factors (a) and (b) in HCA and by identifyinga few most insightful HC scenarios at the cost of domain knowledge. We proposea data-driven HCA framework and introduce active learning in HCA to effectivelyexplore scenarios. Active learning in HCA and characteristics of HC withrespect to the two factors (a) and (b) are illustrated in a 3-bus example.Next, detailed large-scale studies are proposed to understand the significanceof (a) and (b). Our findings suggest that HC and its interpretationssignificantly change subject to the two factors (a) and (b).

An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems