Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling. (arXiv:2208.00728v1 [eess.SY])

Taking advantage of their data-driven and model-free features, Deep
Reinforcement Learning (DRL) algorithms have the potential to deal with the
increasing level of uncertainty due to the introduction of renewable-based
generation. To deal simultaneously with the energy systems' operational cost
and technical constraints (e.g, generation-demand power balance) DRL algorithms
must consider a trade-off when designing the reward function. This trade-off
introduces extra hyperparameters that impact the DRL algorithms' performance
and capability of providing feasible solutions. In this paper, a performance …

algorithms arxiv comparison deep rl energy performance rl scheduling systems