Best-Response Planning of Thermostatically Controlled Loads under Power Constraints
Authors: Frits de Nijs, Matthijs Spaan, Mathijs de Weerdt
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally compare several methods to plan with arbitrage, and conclude that a best response-like mechanism is a scalable approach that returns near-optimal solutions. |
| Researcher Affiliation | Academia | Delft University of Technology, The Netherlands |
| Pseudocode | No | The paper describes methods verbally and through mathematical formulations but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any specific repository link, explicit code release statement, or mention code in supplementary materials for the methodology described. |
| Open Datasets | No | The paper refers to a 'simulated neighborhood' with parameters modeled after a technical report, but does not provide concrete access information (link, DOI, repository, or a formal citation to a public dataset) for the simulated data used. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper mentions computation times but does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions types of solvers and discretization but does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We apply optimal MMDP and MIP solvers to this problem (with the MMDP creating plans for 9 distinct temperature states and θmin = 18.5, θmax = 21.5), as well as the arbitrage decompositions using the Pessimistic probability pon(t) = Lt / n , and the decomposition using the Adaptive probability pon(pj, i, t) = rcpj ,on,i,t / rqpj ,on,i,t (using 10 iterations, each with 10 simulations). ... For the adaptive decomposition we discretized temperature from 16 to 24 degrees over 80 states, resulting in bins of 0.1 degree width. |