Primal-Dual Deep Reinforcement Learning-Based Energy Management of Microgrids with Electric Vehicles Charging Stations
Bibliographic record
Abstract
This paper presents a safe deep reinforcement learning (DRL) framework for the management of low-carbon energy in microgrids (MGs) that integrate renewable energy sources (RESs) and electric vehicle charging stations (EVCS). The inherent variability of RESs, combined with the high power demands of EVCS, poses significant challenges to the real-time operation of MGs, thereby necessitating advanced optimization techniques. Although DRL methodologies offer data-driven approaches to facilitate adaptive decision-making, current penaltybased strategies frequently fall short in effectively enforcing operational constraints under complex scenarios. To address these challenges, we propose a primal-dual DRL methodology utilizing the proximal policy optimization (PD-PPO) algorithm, which incorporates operational constraints directly into the policy learning framework. The MG energy management problem is formulated as a constrained Markov decision process (CMDP), which encompasses distributed generators, RESs, and the demands of EVCS. The PD-PPO algorithm guarantees stable policy updates and ensures robust constraint satisfaction without the need for penalty coefficient adjustments. Comprehensive numerical experiments based on real-world datasets for renewable energy generation and electricity demand demonstrate the efficacy of the proposed approach in achieving low-carbon objectives while optimizing MG operations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".