Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach

TL;DR

This paper introduces a Monte Carlo Tree Search-based model for long-term, geographically flexible resource planning that significantly reduces carbon emissions in power systems with dispersed renewables.

Contribution

It presents a novel MCTS approach for large-scale, long-term power system planning that optimizes resource siting and operation to minimize carbon emissions.

Findings

Reduces over 10% of system-wide carbon emissions.

Achieves up to 8.1X faster solutions than Gurobi.

Maintains solution quality with less than 1.5% gap.

Abstract

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.