Deep reinforcement learning for economic battery dispatch: A comprehensive comparison of algorithms and experiment design choices
Bibliographic record
Abstract
Deep reinforcement learning (DRL) is an increasingly popular optimization tool for the economic dispatch of battery energy systems. However, it remains largely unclear which DRL models, experiment setups, and hyperparameters are most effective, due to contradictory results in the literature and a lack of thorough benchmarks. To address this, we compare popular DRL models and experimental design choices for battery dispatch tasks. We first formulate two battery dispatch tasks that reflect the cross section of existing approaches: energy arbitrage and load following combined with improved renewable energy utilization. We then benchmark four DRL models on case studies in Canada and Germany. Besides the choice of DRL model, we conduct a comprehensive comparison of these experiment design choices: continuous and discrete action spaces, time counters added to the state space (e.g. for the hour of the day), observation stacking, and reward function penalties. Our results show that time counters and observation stacking reliably improve DRL performance. For time counters, cyclic encodings through sine and cosine waves worked best, especially if multiple counters were combined. Reward penalties were less beneficial and barely increased rewards. Deep Q-Networks (DQN) outperformed the other DRL models and significantly benefited from the tested features, increasing rewards by 51% and 20% on the respective case studies. This research highlights the need to carefully choose DRL models, experiment design, and hyperparameters. Our comparative analysis provides a solid foundation for future research on DRL for battery dispatch. By building upon our findings, researchers and practitioners can streamline the experiment design process and obtain better outcomes. • Application of deep reinforcement learning to economic battery dispatch. • Tasks analyzed include energy arbitrage, load following, renewable energy control. • Benchmark of algorithms, deep Q-networks was found to perform best. • Cyclic encoded time features and observation stacking greatly increased rewards. • With the right setup, reinforcement learning can outperform oracles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".