Effective Management of Electric Vehicle Storage Using Smart Charging
Authors: Konstantina Valogianni, Wolfgang Ketter, John Collins, Dmitry Zhdanov
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Evaluation We evaluate AMEVS in different populations and examine its effect on the individual demand curve but also on an aggregate level of peak demand and price reduction. We see that the adoption of AMEVS by all the customer agents leads to peak demand and price reduction on the market. |
| Researcher Affiliation | Academia | Konstantina Valogianni and Wolfgang Ketter Erasmus University Rotterdam {kvalogianni,wketter}@rsm.nl John Collins University of Minnesota jcollins@cs.umn.edu Dmitry Zhdanov University of Connecticut Dmitry.Zhdanov@business.uconn.edu |
| Pseudocode | Yes | Table 1: Adaptive Management of EV Storage AMEVS |
| Open Source Code | No | The paper does not provide any links to open-source code or state that the code is publicly available. |
| Open Datasets | No | We use the household consumption data from the Netherlands obtained in collaboration with a European Utility Company... The customers driving profiles come from the statistical model trained on Dutch mobility data (Dutch Statistics Office (CBS)). |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test splits, specific percentages, or cross-validation setup for reproducibility. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or processor types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions Power TAC as a simulation environment and Reinforcement Learning techniques, but it does not specify versions for any software components, libraries, or programming languages used. |
| Experiment Setup | No | The paper describes the algorithm and models, but it does not provide specific hyperparameter values (e.g., learning rates, batch sizes, epochs) or detailed system-level training settings for experimental setup. |