Bibliographic record
Abstract
In this paper, we show that a group of Demand Response (DR) providing customers will benefit by transferring into a cooperative. While the benefit of aggregation is definite when unconstrained, this paper is devoted to studying benefits of a DR cooperative with a nonlinear piecewise objective function of minimizing net cost comprising energy charges and demand charges while being constrained by power system constraints and other physical constraints. We propose a two-level stochastic optimization formulation that provides a model for DR cooperatives to aggregate and schedule participating resources with the objective of minimizing the total electricity bill comprising energy and demand charges. It also provides a settlement tool for the cooperative to share costs and benefits among its members. In the first level, a stochastic mixed integer linear optimization formulation to minimize the total resource cost for the electricity market is solved considering various probabilistic scenarios to find locational marginal prices (LMPs) for 24-hours and the corresponding amount of DR scheduled for DR participants including the cooperative. In the second level, a linear optimization formulation is solved to schedule DR of cooperative members with the objective of minimizing the total electricity bill of the cooperative comprising energy and demand charges. Finally, we present a settlement tool to share the costs and benefits amongst the cooperative members, whereby all members benefit from reduced electricity bills. The proposed model was tested on modified IEEE 6-bus and 118-bus systems. The results conclusively demonstrate the benefit of the proposed cooperative model for DR where scheduling DR in concert by members has a pronounced effect in reducing demand charges for all more than that achieved by individual members minimizing their own electricity bills.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".