A multi-level optimization technique for electricity consumption management and pricing in residential neighborhoods
Bibliographic record
Abstract
With the introduction of new technologies, concepts and approaches in power transmission, distribution and utilization such as Smart Grids (SG), Advanced Metering Infrastructures (AMI), Distributed Energy Resources (DER) and Demand Side Management (DSM), new capabilities have emerged that enable efficient use and management of power consumption. These capabilities are applicable at micro level in households and building complexes as well as at macro level for utility providers in form of resource and revenue management initiatives. On the other hand, integration of Information Technology (IT) and instrumentation has brought Building Management Systems (BMS) to our homes and has made it possible for the ordinary users to take advantage of more complex and sophisticated energy and cost management features as an integral part of their BMS. The idea of combining capabilities and advantages offered by SG, AMI, DER, DSM and BMS is the backbone of this thesis and has resulted in developing a unique, two-level optimization method for effective deployment of DSM at households and residential neighborhoods. The work consists of an optimization algorithm for households to maximize utilization of DER as the lower level of the envisioned two-level optimization technique while using a customized Game Theoretic optimization for optimizing revenue of utility providers for residential neighborhood as the upper level. This work will also introduce a power management unit, called Load Moderation Center (LMC), to host the developed optimization algorithms as an integrated part of BMS. LMC, upon successful completion, will be able to automatically plan consumption, effectively utilize available sources including grid, renewable energies and storages, and eliminate the need for residences to manually program their BMS for different market scenarios.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".