Multi-agent deep reinforcement learning with online and fair optimal dispatch of EV aggregators
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Bibliographic record
Abstract
The growing popularity of electric vehicles (EVs) and the unpredictable behavior of EV owners have attracted attention to real-time coordination of EVs charging management. This paper presents a hierarchical structure for charging management of EVs by integrating fairness and efficiency concepts within the operations of the distribution system operator (DSO) while utilizing a multi-agent deep reinforcement learning (MADRL) framework to tackle the complexities of energy purchasing and distribution among EV aggregators (EVAs). At the upper level, DSO calculates the maximum allowable power for each EVA based on power flow constraints to ensure grid safety. Then, it finds the optimal efficiency-jain tradeoff (EJT) point, where it sells the highest energy amount while ensuring equitable energy distribution. At the lower level, initially, each EVA acts as an agent employing a double deep Q-network (DDQN) with adaptive learning rates and prioritized experience replay to determine optimal energy purchases from the DSO. Then, the real-time smart dispatch (RSD) controller prioritizes EVs for energy dispatch based on relevant EVs information. Findings indicate the proposed enhanced DDQN outperforms deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO) in cumulative rewards and convergence speed. Finally, the framework’s performance is evaluated against uncontrolled charging and the first come first serve (FCFS) scenario using the 118-bus distribution system, demonstrating superior performance in maintaining safe operation of the grid while reducing charging costs for EVAs. Additionally, the framework’s integration with renewable energy sources (RESs), such as photovoltaic (PV), demonstrates its potential to enhance grid reliability. • Introduces a scalable MADRL framework for real-time EV charging and energy distribution. • Ensures fairness via an Efficiency-Jain Tradeoff (EJT) strategy at the DSO level. • Enhances agent convergence with DDQN using adaptive learning rates and prioritized replay. • Preserves stakeholder privacy with decentralized control and minimal data sharing. • Balances grid reliability with equitable energy allocation under dynamic uncertainties.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it