Hierarchical Decision Making In Electricity Grid Management

Authors: Gal Dalal, Elad Gilboa, Shie Mannor

ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We compare our results to prevailing heuristics, and show the strength of our method. and In this section we show results of IAPI algorithm on the IEEE RTS-96 test system
Researcher Affiliation Academia Gal Dalal GALD@TX.TECHNION.AC.IL Elad Gilboa EGILBOA@TX.TECHNION.AC.IL Shie Mannor SHIE@EE.TECHNION.AC.IL Technion, Israel
Pseudocode Yes Algorithm 1 IAPI Algorithm (followed by a structured algorithm block with Input, Output, steps).
Open Source Code Yes The code for the simulation environment is available at https://github.com/galdl/icml16_iapi.
Open Datasets Yes We use daily demand and wind profiles based on real historical records as published in (Pandzic et al., 2015). and In our simulation we use Nepisodes = 50 episodes, each with a 3 day horizon.
Dataset Splits No The paper does not explicitly describe a validation set or split for hyperparameter tuning or early stopping.
Hardware Specification No The DA policies are evaluated in parallel, on a 200 cores cluster. This is a general description and lacks specific hardware details (e.g., CPU/GPU models, memory).
Software Dependencies No The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation.
Experiment Setup Yes In our simulation we use Nepisodes = 50 episodes, each with a 3 day horizon. and In each cross-entropy iteration we evaluate 200 DA policies (N = 200) and choose the top 20-th percentile for updating Pψ. The DA policies are evaluated in parallel, on a 200 cores cluster. For the TD(0) algorithm we use discounting with γ = 0.95. and Line failure probability pi is set to 5 10 4 for each line, and its time-fill-fix E = 5.