Interface Module for Smart Load Participants in a Transactive Energy Environment- Design and Implementation
Bibliographic record
Abstract
A transactive energy system utilizes the concepts of supply and demand to determine the price power equilibrium of a network. The intersection of the supply and demand relationship represents the equilibrium point of the economy due to allocation of resources in the most efficient way, according to microeconomic theory. Smart devices, part of the transactive energy system generate their willingness to produce and/or consume power in the form of a bid. This bid is represented as a power versus price relationship. The transactive energy system works on aggregating all the bid prices to determine the equilibrium price for the whole network. The equilibrium price is the point at which the net power is zero for the network. It is at this point that the system is balanced, the network is consuming the same amount of power as it is producing. After determining the equilibrium price, it is communicated to all the smart devices. The smart devices then begin to consume or produce energy. Bid and equilibrium prices are sent and received at time intervals, thereby balancing the network at the set time intervals. This is crucial to create an energy efficient system and achievable by developing a control module of smart devices. Such devices can be controlled by obtaining their characteristics, which are then communicated to Internet of Things (IoT) devices. IoT devices, such as micro-controllers, enable remote control and communication with smart devices. This paper proposes a transactive interface module hosted by a cloud server to interact with the energy transactions of the transactive energy system. A practical and economic design for the communication, control and measurement subsystems of the interfacing module including its hardware and software implementation are developed and presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".