An Exploratory Study of the Perception of Peer-to-Peer Energy Trading within the Power Distribution Network in the UAE
Bibliographic record
Abstract
The introduction of Smart Grid resulted in the development of various applications that are built upon the concept of bi-directional flow of electricity and data. One of the Smart Grids pillars is the Distributed Generation (DG) technologies, where customers turn to be prosumers with power generation capability. Another pillar is the Demand Side Management (DSM), which helps control the energy consumption by changing the power usage slots among other peers. DG and DSM have facilitated the sharing of excess power by customers to the grid, and then to their peers through the grid as a trading agent. Although the concept of integrating Peer-to-Peer energy trading with DSM has been explored by scholars and relatively established trading frameworks, there are very limited research performed in respect to the UAE market in terms of its acceptance and readiness towards this energy trading market. This research aims to explore the perception of Peer-to-Peer electricity trading within the Power Distribution Network in the United Arab Emirates. The study will review the Smart Grid network in the UAE and will obtain insights on people’s perception of the transition from classical electricity network to Smart Grid. It will also look into peoples’ perception regarding the transition from being electricity consumers to being electricity producers that trade among peers through semi-structured interviews. This will enhance the understanding of the energy trading market between self-generated power producers connected to a network grid, where the consumer will be utilizing the excess power available in the form of electricity trading, by importing and exporting power, without adding any additional power to the grid. The outcome of the study will provide an insight on the UAE electricity market by designing an electricity trading model that is built upon the following vital factors: power quality, supply reliability, type of integration, peers, and trading time. Furthermore, the study will provide a foundation base to the utilities, as well as individuals, when dealing with the changes in the electricity market structure.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".