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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".