Is it worthwhile to participate in transactive energy? A decision-making model for empowering residential customers
Bibliographic record
Abstract
The deployment of transactive energy systems hinges on well-defined policies that govern the decisions of transactive agents. Traditionally, upper-level agents, such as distribution system operators, aggregators, or coordinators, assume perpetual acceptance and participation by lower-level agents, like residential customers, in new demand-side programs. This assumption, alongside the presumption of agents’ benevolent behavior in a transactional environment, often overlooks the potential for false information in electricity markets, leading to significant economic losses and program failures. To address these challenges, we develop a transactive energy system based on mechanism design, structured around four comprehensive phases: Enrollment, Coordination, Execution, and Settlement. Customers adopt a decision-making model grounded in convex stochastic programming, enabling them to freely choose their daily enrollment in a demand response program and define their willingness to coordinate day-ahead electricity consumption once the Enrollment phase is cleared. The payment rule proposed in this work, which includes a penalty policy for energy deviations, ensures truthful information reporting from residential agents to the coordinator within a negotiation environment. Our results demonstrate that residential agents’ enrollment decisions vary according to the penalty values defined by the coordinator. Additionally, the number of customers enrolled in the Coordination phase significantly influences the coordinator’s daily profits. The study also highlights how electricity deviations during the Execution phase can increase customers’ costs beyond initial expectations, emphasizing the importance of adherence to planned consumption for optimal economic outcomes. This research offers a comprehensive transactive energy system that enhances customer participation through the principle of individual rationality and ensures truthful information reporting among agents based on the incentive compatibility concept in a day-ahead electricity market. Then, is it worthwhile to participate in transactive energy? The short answer is yes, and the reasons are unveiled throughout this paper. • An ex-ante individual rationality model is introduced to empower residential agents. • A methodology to select a number of residential agents to coordinate is introduced. • Four contract phases assessed the effect of agents’ decisions in a day-ahead market. • A mechanism is designed to ensure a truthful report of information in transactions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".