Energy Management of Multi-Energy Communities: A Hierarchical MIQP-Constrained Deep Reinforcement Learning Approach
Bibliographic record
Abstract
This paper proposes a hybrid mixed-integer quadratic programming-constrained deep reinforcement learning (MIQP-CDRL) framework for energy management of multi-energy communities. The framework employs a hierarchical two-layer structure: the MIQP layer handles day-ahead scheduling, minimizing operational costs while ensuring system constraint satisfaction, while the CDRL agent makes real-time adjustments. The goal of this framework is to combine the strengths of CDRL in addressing sequential decision-making problems in stochastic systems with the advantages of a mathematical programming model to guide the agent's exploration during the training and reduce the dependency on opaque policies during real-time operation. The system dynamics are modeled as a constrained Markov decision process (CMDP), which is solved by a model-free CDRL agent built upon the constrained policy optimization (CPO) algorithm. Practical test results demonstrate the effectiveness of this framework in improving the optimality and feasibility of the real-time solutions compared to existing stand-alone DRL approaches.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| 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".