  Transmission interface power flow adjustment is a critical measure to ensure
the security and economy operation of power systems. However, conventional
model-based adjustment schemes are limited by the increasing variations and
uncertainties occur in power systems, where the adjustment problems of
different transmission interfaces are often treated as several independent
tasks, ignoring their coupling relationship and even leading to conflict
decisions. In this paper, we introduce a novel data-driven deep reinforcement
learning (DRL) approach, to handle multiple power flow adjustment tasks jointly
instead of learning each task from scratch. At the heart of the proposed method
is a multi-task attribution map (MAM), which enables the DRL agent to
explicitly attribute each transmission interface task to different power system
nodes with task-adaptive attention weights. Based on this MAM, the agent can
further provide effective strategies to solve the multi-task adjustment problem
with a near-optimal operation cost. Simulation results on the IEEE 118-bus
system, a realistic 300-bus system in China, and a very large European system
with 9241 buses demonstrate that the proposed method significantly improves the
performance compared with several baseline methods, and exhibits high
interpretability with the learnable MAM.
