Cost-Aware Reinforcement Learning with Action Masking and Projection for Battery Energy Storage Dispatch under Suppressed-Spread Market Shifts

📅 2026-09-20
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🤖 AI Summary
研究使用PPO控制器结合行动屏蔽和投影方法,解决电池储能系统在价格差缩小情况下的成本意识调度问题。
📝 Abstract
Battery energy storage system (BESS) dispatch must preserve operational feasibility while declining price spreads reduce the margin available to pay for cycling. We study a proximal policy optimization (PPO) controller whose pre-selection physical action mask and emergency projection are separated from a causal, forecast-informed economic advisory. All forecast-dependent methods receive the same causal 24-step forecast and grid-side settlement. Across five PPO seeds, advice-on net profit is 30.59 and 18.04 USD per 336-hour T1 and T2 window, versus 36.77 and 22.94 USD for proxy-cost MPC; PPO remains below this reference in both periods. Advice raises T2 profit from 16.45 to 18.04 USD while reducing throughput, but is immaterial in T1. On disjoint weekly blocks, PPO is stable under daily, weekly, and blended seasonal forecasts, weakens under persistence, and remains below proxy-cost MPC. Paired diagnostics localize changes to the observed 5-10 USD/MWh regime with mixed SoC-dependent effects. An M0-M6 ablation shows that mask removal sends thousands of infeasible requests to projection, while removing both physical layers exposes ramp violations. The evidence separates economic screening from feasibility enforcement without claiming formal safety, lifecycle-optimal aging, or RL dominance.
Problem

Research questions and friction points this paper is trying to address.

Battery Energy Storage System
Cost-Aware Reinforcement Learning
Action Masking
Price Spreads
Operational Feasibility
Innovation

Methods, ideas, or system contributions that make the work stand out.

Cost-Aware Reinforcement Learning
Action Masking
Projection
Battery Energy Storage System (BESS)
Proximal Policy Optimization (PPO)
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Kuanlin Chen
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Chen-Wei Kuo
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