Control Policy Correction Framework for Reinforcement Learning-based Energy Arbitrage Strategies

arXiv:2404.18821v1 Announce Type: cross
Abstract: A continuous rise in the penetration of renewable energy sources, along with the use of the single imbalance pricing, provides a new opportunity for balance responsible parties to reduce their cost through energy arbitrage in the imbalance settlement mechanism. Model-free reinforcement learning (RL) methods are an appropriate choice for solving the energy arbitrage problem due to their outstanding performance in solving complex stochastic sequential problems. However, RL is rarely deployed in real-world applications since its …

abstract arxiv balance continuous control cost cs.ai cs.lg cs.sy eess.sy energy framework free parties policy pricing reduce reinforcement reinforcement learning renewable responsible settlement strategies through type