Demand Response Under Stochastic, Price-Dependent User Behavior
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917348785520640 |
|---|---|
| author | Cavraro, Guido Bernstein, Andrey Dall'Anese, Emiliano |
| author_facet | Cavraro, Guido Bernstein, Andrey Dall'Anese, Emiliano |
| contents | This paper focuses on price-based residential demand response implemented through dynamic adjustments of electricity prices during DR events. It extends existing DR models to a stochastic framework in which customer response is represented by price-dependent random variables, leveraging models and tools from the theory of stochastic optimization with decision-dependent distributions. The inherent epistemic uncertainty in the customers' responses renders open-loop, model-based DR strategies impractical. To address this challenge, the paper proposes to employ stochastic, feedback-based pricing strategies to compensate for estimation errors and uncertainty in customer response. The paper then establishes theoretical results demonstrating the stability and near-optimality of the proposed approach and validates its effectiveness through numerical simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15983 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Demand Response Under Stochastic, Price-Dependent User Behavior Cavraro, Guido Bernstein, Andrey Dall'Anese, Emiliano Systems and Control This paper focuses on price-based residential demand response implemented through dynamic adjustments of electricity prices during DR events. It extends existing DR models to a stochastic framework in which customer response is represented by price-dependent random variables, leveraging models and tools from the theory of stochastic optimization with decision-dependent distributions. The inherent epistemic uncertainty in the customers' responses renders open-loop, model-based DR strategies impractical. To address this challenge, the paper proposes to employ stochastic, feedback-based pricing strategies to compensate for estimation errors and uncertainty in customer response. The paper then establishes theoretical results demonstrating the stability and near-optimality of the proposed approach and validates its effectiveness through numerical simulations. |
| title | Demand Response Under Stochastic, Price-Dependent User Behavior |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2603.15983 |