Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Incentivizing Intelligent Customer Behavior in Smart-Grids: A Risk-Sharing Tariff & Optimal Strategies
Authors: Georgios Methenitis, Michael Kaisers, Han La Poutré
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Within a game-theoretical analysis, we capture the strategic conflict of interest between a retailer and a customer in a two-player game, and we present optimal, i.e., best response, strategies for both players in this game. We show analytically that the proposed tariff provides customers of varying flexibility with variable incentives to assume and alleviate a fraction of the balancing risk, contributing in this way to the uncertainty reduction in the envisioned smart-grid. |
| Researcher Affiliation | Academia | Georgios Methenitis , Michael Kaisers , Han La Poutre Centrum Wiskunde & Informatica Delft University of Technology EMAIL |
| Pseudocode | No | The paper describes mathematical models and theoretical concepts but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide information about open-source code availability for the described methodology. |
| Open Datasets | No | The paper uses theoretical distributions (e.g., fx = N(0.15, 0.1)) for its numerical illustrations rather than publicly available datasets for training. |
| Dataset Splits | No | The paper uses theoretical distributions for its analyses and computations, but does not provide specific dataset split information for reproduction. |
| Hardware Specification | No | The paper does not specify any hardware used for computations. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers. |
| Experiment Setup | No | The paper specifies parameters for its theoretical models (e.g., fx = N(0.15, 0.1), p = 0.1, p0 = 0.5, ' = 0.02, # = 1, w = 10) for numerical illustrations, but these are model parameters, not experimental setup details like hyperparameters or training configurations for an empirical study. |