Hierarchical Decision Making In Electricity Grid Management
This addresses grid management challenges for energy operators, though it appears incremental as it applies RL to a known bottleneck.
The paper tackles the problem of managing electricity grid reliability under uncertainty from renewable energy and variable demand by introducing a hierarchical decision-making model using reinforcement learning, showing improved performance compared to existing heuristics.
The power grid is a complex and vital system that necessitates careful reliability management. Managing the grid is a difficult problem with multiple time scales of decision making and stochastic behavior due to renewable energy generations, variable demand and unplanned outages. Solving this problem in the face of uncertainty requires a new methodology with tractable algorithms. In this work, we introduce a new model for hierarchical decision making in complex systems. We apply reinforcement learning (RL) methods to learn a proxy, i.e., a level of abstraction, for real-time power grid reliability. We devise an algorithm that alternates between slow time-scale policy improvement, and fast time-scale value function approximation. We compare our results to prevailing heuristics, and show the strength of our method.