Complexity of Scheduling Charging in the Smart Grid
Authors: Mathijs de Weerdt, Michael Albert, Vincent Conitzer, Koos van der Linden
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental study establishes up to what parameter values the dynamic programs can determine optimal solutions in a couple of minutes. and In the following experimental analysis we aim to establish when runtimes are sufficiently large that there is significant value in researching faster (but possibly non-optimal) algorithms instead of straightforwardly using the proposed dynamic programs. |
| Researcher Affiliation | Academia | Mathijs de Weerdt1, Michael Albert2, Vincent Conitzer2, and Koos van der Linden1 1 Delft University of Technology, Delft, The Netherlands 2 Duke University, Durham, North Carolina, USA |
| Pseudocode | Yes | DP1 : OPT(m1, m2, . . . , m|T |, i) = ( 0 if i = 0 max OPT(m1, m2, . . . , m|T |, i 1), o otherwise max a1,.,a|T | OPT(m1 a1, . . . , m|T | a|T |, i 1) + vi (a) . and 1. Sort all charging task triples on deadline (increasing, with arbitrary tie-breaking). 2. Let M1, M2, . . . , Mn be the cumulative supply at the deadlines of tasks 1, 2, . . . , n that is, Mi = Pdi t=1 mt and let M0 = 0. 3. Run a DP based on the following recursion (where m denotes the remaining cumulative supply available for the first i tasks): OPT(m, i) = ... |
| Open Source Code | No | No explicit statement or link providing concrete access to the source code for the methodology described in the paper was found. |
| Open Datasets | Yes | to model this, we use load measurements of a household with 15 minute resolution from the Pecan Street dataset [Pecan Street Inc., 2017]. |
| Dataset Splits | No | The paper describes how problem instances are generated randomly with certain parameters, but it does not specify explicit train/validation/test dataset splits or cross-validation setup for these instances or the Pecan Street dataset. |
| Hardware Specification | Yes | on a standard desktop computer (in our case containing an i7-6700 3.4Ghz quad core processor with 32GB of memory) |
| Software Dependencies | No | The paper states, 'We implemented both DP1 and DP2 in python using a single threaded tabulation approach,' but it does not specify the Python version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We start with relatively small problem instances, using the following default settings (unless stated otherwise): there are 4 agents, the horizon is 6 time steps, supply at each time step is scaled into the range of 3 times the maximum charging speed, and the maximum demand per vehicle is equivalent to charging 3 time steps at full speed. The number of deadlines is either 1 (single) or 3 (multiple deadlines). |