  With exponential growth in distributed energy resources (DERs) coupled with
at-capacity distribution grid infrastructure, prosumers cannot always export
all extra power to the grid without violating technical limits. Consequently, a
slew of dynamic hosting capacity (DHC) algorithms have emerged for optimal
utilization of grid infrastructure while maximizing export from DERs. Most of
these DHC algorithms utilize the concept of operating envelopes (OE), where the
utility gives prosumers technical power export limits, and they are free to
export power within these limits. Recent studies have shown that OE-based
frameworks have drawbacks, as most develop power export limits based on convex
or linear grid models. As OEs must capture extreme operating conditions, both
convex and linear models can violate technical limits in practice because they
approximate grid physics. However, AC models are unsuitable because they may
not be feasible within the whole region of OE. We propose a new two-stage
optimization framework for DHC built on three-phase AC models to address the
current gaps. In this approach, the prosumers first run a receding horizon
multi-period optimization to identify optimal export power setpoints to
communicate with the utility. The utility then performs an infeasibility-based
optimization to either accept the prosumer's request or dispatch an optimal
curtail signal such that overall system technical constraints are not violated.
To explore various curtailment strategies, we develop an L1, L2, and Linf
norm-based dispatch algorithm with an exact three-phase AC model. We test our
framework on a 1420 three-phase node meshed distribution network and show that
the proposed algorithm optimally curtails DERs while guaranteeing the AC
feasibility of the network.
