  Global climate challenge is demanding urgent actions for decarbonization,
while electric power systems take the major roles in clean energy transition.
Due to the existence of spatially and temporally dispersed renewable energy
resources and the uneven distribution of carbon emission intensity throughout
the grid, it is worth investigating future load planning and demand management
to offset those generations with higher carbon emission rates. Such techniques
include inter-region utilization of geographically shiftable resources and
stochastic renewable energy. For instance, data center is considered to be a
major carbon emission producer in the future due to increasing information
load, while it holds the capability of geographical load balancing. In this
paper, we propose a novel planning and operation model minimizing the
system-level carbon emissions via sitting and operating geographically
shiftable resources. This model decides the optimal locations for shiftable
resources expansion along with power dispatch schedule. To accommodate future
system operation patterns and a wide range of operating conditions, we
incorporate 20-year fine-grained load and renewables scenarios for grid
simulations of realistic sizes (e.g., up to 1888 buses). To tackle the
computational challenges coming from the combinatorial nature of such
large-scale planning problem, we develop a customized Monte Carlo Tree Search
(MCTS) method, which can find reasonable solutions satisfying solution time
limits. Besides, MCTS enables flexible time window settings and offline
solution adjustments. Extensive simulations validate that our planning model
can reduce more than 10\% carbon emission across all setups. Compared to
off-the-shelf optimization solvers such as Gurobi, our method achieves up to
8.1X acceleration while the solution gaps are less than 1.5\% in large-scale
cases.
