A Dynamic Retail Market Model to Investigate Sustainability of Retail Contracts in DERs-Penetrated Markets
Bibliographic record
Abstract
The increasing penetration of behind-the-meter (BTM) distributed energy resources (DERs) in the electricity grid will reduce the utilities’ net demand and increase customers’ profits through reduced electricity bills and compensation for excess generation via mechanisms such as net-metering credits. To recover lost revenue, distribution utilities, as asset operators—and retailers, as energy procurement entities, are often compelled to raise electricity prices. This, in turn, further incentivizes DERs adoption, potentially leading to a feedback loop of rising rates and declining demand that threatens the long-term financial sustainability of traditional utility models. In this context, there is a gap for innovative retailer business models in the era of increasing DERs. This paper focuses on identifying the dynamics underlying retail market operations and business sustainability in the era of increasing DERs. Accordingly, this paper proposes a dynamic retail market model that captures the interdependencies of market components and processes through non-linear causal relationships and feedback loops. This enables retailers to investigate their long-term business performance in prosumer-penetrated networks. Additionally, this work develops an integrated operation and planning framework for the techno-economic analysis and decision modeling of retailers, utilities, and customers in the retail market paradigm. Using the developed framework, this work further proposes two alternative retailer business models that enhance retailers’ long-term business sustainability and customers’ economic viability. Analytical studies evaluate the business models and present recommendations to ensure the financial sustainablility. The results demonstrate that the proposed subscription-based models can successfully mitigate the adverse financial effects of widespread DERs adoption and ensure long-term system stability.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".