Layered stochastic approach for residential demand response based on real‐time pricing and incentive mechanism
Bibliographic record
Abstract
This paper proposes a layered stochastic optimization approach for residential demand response (DR) under real‐time pricing (RTP) and an incentive‐based mechanism, which contains three steps. In the first layer, an independent system operator (ISO) announces day‐ahead RTP to a residential load aggregator (RLA). The RLA predicts individual household loads (step 1) and aggregates the loads to minimize electrical cost (step 2). In the second layer, the RLA announces incentives to homes, and home energy management systems (EMS) control the loads to maximize the reward in real‐time (step 3). In Step 1, probability based individual load prediction models are developed. In Step 2, a stochastic optimization model is developed to aggregate controllable loads of residential consumers. In Step 3, an incentive‐based mechanism is proposed, based on which, a real‐time load control model for individual homes is developed to benefit the RLA and homeowners. A highly efficient real‐time control algorithm for home EMS is developed. The case studies show that, with 10% controllable energy integration, the peak demand is reduced by 17.5% and the energy cost of the controllable loads is reduced by 28%. The proposed mechanism can effectively aggregate many individual residential controllable loads to participate in an electricity market.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".