Energy Management of Net-Zero Multi-energy Microgrids: An Interior-Point Safe Deep Reinforcement Learning Approach
Bibliographic record
Abstract
This paper presents a safe deep reinforcement learning (SDRL) framework to optimize the operation of multi-energy microgrids (MEMGs) aimed at achieving net-zero emissions. Traditional DRL methods for energy management in MEMGs often struggle to address complex constraints directly, particularly when incorporating carbon capture systems (CCS) and participating in the cap-and-trade (C&T) carbon market. To overcome these limitations, we propose an interior-point optimization with a proximal policy optimization (IPO-PPO) approach. This novel integration enables direct constraint management without relying on penalty tuning, thus enhancing policy stability and reducing computational overhead. Our method formulates the MEMG management problem as a constrained Markov decision process (CMDP), capturing the stochastic and dynamic nature of energy sources, storage, and consumption. Validation with real-world data demonstrates that the IPO-PPO framework improves operational efficiency, minimizes greenhouse gas emissions, and ensures strict constraint adherence across various scenarios.
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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 | 0.002 |
| 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".