Deep Reinforcement Learning for Direct Load Control in Distribution Networks
Bibliographic record
Abstract
Direct load control enables load aggregators in distribution networks to remotely curtail customers' appliances during peak time periods. This paper proposes a direct load control algorithm for residential customers, while accounting for the uncertainties in the customers' discomfort from curtailing their demand as well as the operational constraints imposed by the distribution network. We model the load control problem as a Markov decision process (MDP). Solving such an MDP is challenging due to the ac power flow equations and the unknown dynamics of the system states (i.e., price, demand, and customer's discomfort). We develop a deep reinforcement learning algorithm based on the actor-critic method that enables the load aggregator to consider the distribution network constraints and the consequences of its past decisions to update the neural network parameters for the policy and value function without any knowledge of the system dynamics. Simulations are performed on an IEEE 85-bus test feeder with 59 households. Results show that the load aggregator learns to reduce the peak load by 16.7%, while taking into account the distribution network constraints. Also, the customers' cost is decreased by 26.6% on average; thereby reaching a win-win outcome.
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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".