Congestion Games for V2G-Enabled EV Charging
Authors: Benny Lutati, Vadim Levit, Tal Grinshpoun, Amnon Meisels
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A detailed empirical evaluation assesses the performance of the iterated best-response process. The evaluation considers the quality of the resulting solutions and the rate of convergence to a stable state. |
| Researcher Affiliation | Academia | 1Department of Computer Science Ben-Gurion University of the Negev, Be er-Sheva, Israel 2Department of Industrial Engineering and Management Ariel University, Ariel, Israel |
| Pseudocode | Yes | Algorithm 1 Find Best Response (ai, di, qi, d) |
| Open Source Code | No | The paper does not provide any statements or links indicating that its source code is publicly available. |
| Open Datasets | No | The problems used in this evaluation were randomly generated according to the following process. First, the number of agents V and time-slots T were given to each experiment as parameters. Next, a background power load was randomly selected for each time-slot from the range [0, |V |/2]. Then, the EVs preferences were generated by randomly selecting the arrival and departure times (in the range [0, |T|]), as well as the amount of energy units that each EV needs to charge. This amount was deļ¬ned by a natural number randomly selected from the range [0, 100]. All selections were made with uniform distribution. The paper does not provide access information for this generated data. |
| Dataset Splits | No | The paper describes generating random problems and instances for evaluation but does not specify any training, validation, or test dataset splits or cross-validation setups. It refers to "200 randomly generated problems" for evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies or version numbers (e.g., programming languages, libraries, or solvers with versions) used for the implementation or experiments. |
| Experiment Setup | No | The paper describes how the input problems were generated (e.g., number of agents, time-slots, background load) and discusses player ordering for convergence (Round-robin, Expensive first). However, it does not specify concrete hyperparameters or system-level training settings typically found in experimental setups for models (e.g., learning rates, batch sizes, number of epochs, optimizer details). |