Preference Aware Battery Dispatch Learning Extracts Grid Operator Choices from Historical Logs and Schedules Operational Data

Abstract

The integration of battery energy storage systems into modern power grids has necessitated the development of advanced dispatch strategies to balance economic profitability, grid stability, and asset longevity. Traditional optimization techniques frequently rely on rigid, pre-defined objective functions that fail to capture the nuanced, context-dependent decisions made by human grid operators. This paper presents a comprehensive investigation into preference-aware battery dispatch learning, a novel approach that extracts operational policies directly from historical operator choice logs. By leveraging machine learning techniques akin to inverse reinforcement learning and behavioral cloning, we construct a framework that infers the underlying reward structures and operational preferences embedded within years of manual dispatch decisions. This methodology bridges the gap between purely economic algorithmic dispatch and human-centric operational realities, which often prioritize conservative safety margins and equipment health over marginal financial gains under specific market conditions. We systematically analyze a large-scale dataset of operator logs, extracting critical features such as state of charge, market pricing signals, grid frequency deviations, and estimated degradation penalties. The proposed preference-aware models are rigorously evaluated against traditional optimization algorithms and baseline machine learning approaches. Results indicate that our framework not only predicts operator behavior with unprecedented accuracy but also produces automated dispatch policies that are inherently trusted by human supervisors, thereby facilitating smoother transitions toward fully autonomous grid management. This study underscores the critical importance of incorporating human operational preferences into automated energy systems, paving the way for more resilient, adaptable, and socially acceptable artificial intelligence applications in critical infrastructure.