Papers › Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling

Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling

1 Aug 2022arXiv:2208.00728archive 2025-07-28

Hou Shengren, Edgar Mauricio Salazar, Pedro P. Vergara, Peter Palensky

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 comparison of different DRL algorithms, including DDPG, TD3, SAC, and PPO, are presented. We aim to provide a fair comparison of these DRL algorithms for energy systems optimal scheduling problems. Results show DRL algorithms' capability of providing in real-time good-quality solutions, even in unseen operational scenarios, when compared with a mathematical programming model of the energy system optimal scheduling problem. Nevertheless, in the case of large peak consumption, these algorithms failed to provide feasible solutions, which can impede their practical implementation.

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shengrenhou/drl-for-energy-systems-optimal-scheduling officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Deep Reinforcement Learning Reinforcement Learning (RL)Scheduling energy management

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Methods

1x1 Convolution Adam Average Pooling Batch Normalization Clipped Double Q-learning Convolution DDPG Dense Connections Dilated Convolution Entropy Regularization Experience Replay Global Average Pooling PPO ReLU SAC TD3 Target Policy Smoothing Weight Decay

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