On-line reinforcement learning for optimization of real-life energy trading strategy

arXiv:2303.16266v3 Announce Type: replace
Abstract: An increasing share of energy is produced from renewable sources by many small producers. The efficiency of those sources is volatile and, to some extent, random, exacerbating the problem of energy market balancing. In many countries, this balancing is done on the day-ahead (DA) energy markets. This paper considers automated trading on the DA energy market by a medium-sized prosumer. We model this activity as a Markov Decision Process and formalize a framework in which …

abstract arxiv cs.lg efficiency energy life line optimization q-fin.tr random reinforcement reinforcement learning renewable small strategy trading type