Online Mechanism Design for Vehicle-to-Grid Car Parks
Authors: Enrico H. Gerding, Sebastian Stein, Sofia Ceppi, Valentin Robu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We furthermore evaluate the algorithms using simulations, and we show that some of our algorithms benefit significantly from V2G, achieving positive benefit for the car park even when agents do not pay for using it. |
| Researcher Affiliation | Academia | Enrico H. Gerding University of Southampton, UK Sebastian Stein University of Southampton, UK Sofia Ceppi University of Edinburgh, UK Valentin Robu Heriot Watt University, UK |
| Pseudocode | No | The paper describes the algorithms (Algorithm 1, Algorithm 2, Algorithm 3) in paragraph text, but does not provide formal pseudocode blocks or figures. |
| Open Source Code | No | The paper states: "All data supporting this study are openly available from the University of Southampton repository at http://dx.doi.org/10.5258/SOTON/391163." This explicitly refers to data, not source code. |
| Open Datasets | No | The paper describes a simulation setup where data is generated: "We simulate a large car park over a period of 24 hours, where most cars arrive in the morning, representing a typical workplace. Specifically, time is discretised into 1-hourly time slots, and the number of arrivals at every hour is drawn from a Poisson distribution with a mean that varies over time and peaks at 8am." |
| Dataset Splits | No | The paper describes a simulation setup and data generation process, but does not specify dataset splits (training, validation, test) as it is not using a fixed pre-existing dataset. Data is simulated dynamically. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the simulations or experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | Yes | We simulate a large car park over a period of 24 hours, where most cars arrive in the morning, representing a typical workplace. Specifically, time is discretised into 1-hourly time slots, and the number of arrivals at every hour is drawn from a Poisson distribution with a mean that varies over time and peaks at 8am. Unless specified otherwise, the expected number of arrivals over the entire day is 100 cars, and each car stays in the car park for between 1 and 16 hours (drawn uniformly at random) or until midnight, whichever is earlier. We discretise a car s battery into units representing 3 k Wh each, which we assume is the electricity that can be charged or discharged in one hour. To determine the initial state of charge of agent i, So Ci,ai, and its maximum state of charge, So Cmax i , we draw two integers from the interval 1 to 8 and assign the smaller to So Ci,ai and the larger to So Cmax i . This means that vehicles have a battery capacity of up to 24 k Wh, which is the typical capacity of current EVs. The required charge qi is determined by randomly picking a feasible charge given the vehicle s duration of stay and maximum capacity. We assume constant capacity losses for charging and discharging a unit of electricity (a in Section 3) and draw these for each agent from a uniform distribution on [$0.01, $0.03]. Finally, the cost parameter ci is drawn from the interval [1, 5], discretised into steps of 0.1. Grid prices are simulated using a Markov chain that randomly starts in one of four states. Each state s is associated with a buying price pbuy,s (respectively, these are $0.03, $0.15, $1.20 and $3.00 for states 1 4) and a selling price psell,s = 0.75pbuy,s. With probability 0.5, this Markov chain remains in the current state and otherwise transitions to one of its direct neighbours. |