Learning and Optimization for Price-based Demand Response of Electric Vehicle Charging

no code implementations • 16 Apr 2024 • Chengyang Gu, Yuxin Pan, Ruohong Liu, Yize Chen

Thus, to model and optimize EV charging, it is important for charging station operator to model the PBDR patterns of EV customers by precisely predicting charging demands given price signals.

Large Foundation Models for Power Systems

1 code implementation • 12 Dec 2023 • Chenghao Huang, Siyang Li, Ruohong Liu, Hao Wang, Yize Chen

Foundation models, such as Large Language Models (LLMs), can respond to a wide range of format-free queries without any task-specific data collection or model training, creating various research and application opportunities for the modeling and operation of large-scale power systems.

Retrieval Scheduling

Laxity-Aware Scalable Reinforcement Learning for HVAC Control

no code implementations • 29 Jun 2023 • Ruohong Liu, Yuxin Pan, Yize Chen

Demand flexibility plays a vital role in maintaining grid balance, reducing peak demand, and saving customers' energy bills.

reinforcement-learning Reinforcement Learning (RL)

Learning a Multi-Agent Controller for Shared Energy Storage System

no code implementations • 16 Feb 2023 • Ruohong Liu, Yize Chen

Deployment of shared energy storage systems (SESS) allows users to use the stored energy to meet their own energy demands while saving energy costs without installing private energy storage equipment.

Multi-agent Reinforcement Learning Scheduling

Learning Task-Aware Energy Disaggregation: a Federated Approach

1 code implementation • 14 Apr 2022 • Ruohong Liu, Yize Chen

We consider the problem of learning the energy disaggregation signals for residential load data.

Federated Learning Meta-Learning +1