This paper proposes a two-level hierarchical matching framework for
Integrated Hybrid Resources (IHRs) with grid constraints. An IHR is a
collection of Renewable Energy Sources (RES) and flexible customers within a
certain power system zone, endowed with an agent to match. The key idea is to
pick the IHR zones so that the power loss effects within the IHRs can be
neglected. This simplifies the overall matching problem into independent
IHR-level matching problems and an upper-level optimal power flow problem to
meet the IHR-level upstream flow requirements while respecting the grid
constraints. Within each IHR, the agent employs a scalable Deep Reinforcement
Learning algorithm to identify matching solutions such that the customer's
service constraints are met. The central agent then solves an optimal power
flow problem with the IHRs as the nodes, with their active power flow and
reactive power {capacities}, and grid constraints to scalably determine the
final flows such that matched power can be delivered to the extent the grid
constraints are satisfied. The proposed framework is implemented on a test
power distribution system, and multiple case studies are presented to
substantiate the welfare efficiency of the proposed solution and the
satisfaction of the grid and customers' servicing constraints