Search Results for author:
Found 11 papers, 4 papers with code
Unsupervised Learning for Equitable DER Control
In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers.
Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning
In detail, we theoretically demonstrate that the effects of non-stationarity introduced by multiple timescales can be learned by a periodic multi-agent policy.
Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning
Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms.
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems
We present the PowerGridworld software package to provide users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for reinforcement learning (RL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets
Increasing levels of renewable generation motivate a growing interest in data-driven approaches for AC optimal power flow (AC OPF) to manage uncertainty; however, a lack of disciplined dataset creation and benchmarking prohibits useful comparison among approaches in the literature.
Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks
Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms.
PHASED: Phase-Aware Submodularity-Based Energy Disaggregation
Energy disaggregation is the task of discerning the energy consumption of individual appliances from aggregated measurements, which holds promise for understanding and reducing energy usage.
Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition
As electric grids experience high penetration levels of renewable generation, fundamental changes are required to address real-time situational awareness.
GRATE: Granular Recovery of Aggregated Tensor Data by Example
In this paper, we address the challenge of recovering an accurate breakdown of aggregated tensor data using disaggregation examples.
Energy Storage Management via Deep Q-Networks
Energy storage devices represent environmentally friendly candidates to cope with volatile renewable energy generation.
Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection
The resulting community detection approach is successful even when some links in the graphs are missing.
